Valid models in INLA

This page describe the models implemented in inla, divided into sections: latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.

inla.models()

Value

Valid sections are: latent, group, scopy, mix, link, predictor, hazard, likelihood, prior, wrapper, lp.scale.

'latent'

Valid models in this section are:

Model 'linear'.

Properties:

doc =

Alternative interface to an fixed effect

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

linear

Number of hyperparmeters is 0.

Model 'iid'.

Properties:

doc =

Gaussian random effects in dim=1

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

indep

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

1001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'mec'.

Properties:

doc =

Classical measurement error model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

mec

Number of hyperparmeters is 4.

Hyperparameter 'theta1'

hyperid =

2001

name =

beta

short.name =

b

prior =

gaussian

param =

1 0.001

initial =

1

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

2002

name =

prec.u

short.name =

prec

prior =

loggamma

param =

1 1e-04

initial =

9.21034037197618

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

2003

name =

mean.x

short.name =

mu.x

prior =

gaussian

param =

0 1e-04

initial =

0

fixed =

TRUE

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

2004

name =

prec.x

short.name =

prec.x

prior =

loggamma

param =

1 10000

initial =

-9.21034037197618

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'meb'.

Properties:

doc =

Berkson measurement error model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

meb

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

3001

name =

beta

short.name =

b

prior =

gaussian

param =

1 0.001

initial =

1

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

3002

name =

prec.u

short.name =

prec

prior =

loggamma

param =

1 1e-04

initial =

6.90775527898214

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rgeneric'.

Properties:

doc =

Generic latent model specified using R

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

rgeneric

Number of hyperparmeters is 0.

Model 'cgeneric'.

Properties:

doc =

Generic latent model specified using C

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

rgeneric

Number of hyperparmeters is 0.

Model 'rw1'.

Properties:

doc =

Random walk of order 1

constr =

TRUE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

min.diff =

1e-06

pdf =

rw1

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

4001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2'.

Properties:

doc =

Random walk of order 2

constr =

TRUE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

min.diff =

1e-04

pdf =

rw2

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

5001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'crw2'.

Properties:

doc =

Exact solution to the random walk of order 2

constr =

TRUE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

2

aug.constr =

1

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

min.diff =

1e-04

pdf =

crw2

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

6001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'prw2'.

Properties:

doc =

Proper random walk of order 2

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

prw2

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

6103

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

6102

name =

log range

short.name =

range

prior =

pc.prw2.range

param =

0 0 0 0

initial =

3

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'seasonal'.

Properties:

doc =

Seasonal model for time series

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

missing.values.warning =

TRUE

pdf =

seasonal

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

7001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besag'.

Properties:

doc =

The Besag area model (CAR-model)

constr =

TRUE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

besag

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

8001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besag2'.

Properties:

doc =

The shared Besag model

constr =

TRUE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

1 2

n.div.by =

2

n.required =

TRUE

set.default.values =

TRUE

pdf =

besag2

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

9001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

9002

name =

scaling parameter

short.name =

a

prior =

loggamma

param =

10 10

initial =

0

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'bym'.

Properties:

doc =

The BYM-model (Besag-York-Mollier model)

constr =

TRUE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

2

aug.constr =

2

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

bym

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

10001

name =

log unstructured precision

short.name =

prec.unstruct

prior =

loggamma

param =

1 5e-04

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

10002

name =

log spatial precision

short.name =

prec.spatial

prior =

loggamma

param =

1 5e-04

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'bym2'.

Properties:

doc =

The BYM-model with the PC priors

constr =

TRUE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

2

aug.constr =

2

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

bym2

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

11001

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

11002

name =

logit phi

short.name =

phi

prior =

pc

param =

0.5 0.5

initial =

-3

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'besagproper'.

Properties:

doc =

A proper version of the Besag model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

besagproper

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

12001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-04

initial =

2

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

12002

name =

log diagonal

short.name =

diag

prior =

loggamma

param =

1 1

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besagproper2'.

Properties:

doc =

An alternative proper version of the Besag model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

besagproper2

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

13001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-04

initial =

2

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

13002

name =

logit lambda

short.name =

lambda

prior =

gaussian

param =

0 0.45

initial =

3

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'fgn'.

Properties:

doc =

Fractional Gaussian noise model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

5

aug.constr =

1

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

order.default =

4

order.defined =

3 4

pdf =

fgn

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

13101

name =

log precision

short.name =

prec

prior =

pc.prec

param =

3 0.01

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

13102

name =

logit H

short.name =

H

prior =

pcfgnh

param =

0.9 0.1

initial =

2

fixed =

FALSE

to.theta =

function(x) log((2 * x - 1) / (2 * (1 - x)))

from.theta =

function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))

Model 'fgn2'.

Properties:

doc =

Fractional Gaussian noise model (alt 2)

constr =

FALSE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

4

aug.constr =

1

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

order.default =

4

order.defined =

3 4

pdf =

fgn

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

13111

name =

log precision

short.name =

prec

prior =

pc.prec

param =

3 0.01

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

13112

name =

logit H

short.name =

H

prior =

pcfgnh

param =

0.9 0.1

initial =

2

fixed =

FALSE

to.theta =

function(x) log((2 * x - 1) / (2 * (1 - x)))

from.theta =

function(x) 0.5 + 0.5 * exp(x) / (1 + exp(x))

Model 'ar1'.

Properties:

doc =

Auto-regressive model of order 1 (AR(1))

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

missing.values.warning =

TRUE

pdf =

ar1

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

14001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

14002

name =

logit lag one correlation

short.name =

rho

prior =

normal

param =

0 0.15

initial =

2

fixed =

FALSE

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta3'

hyperid =

14003

name =

mean

short.name =

mean

prior =

normal

param =

0 1

initial =

0

fixed =

TRUE

to.theta =

function(x) x

from.theta =

function(x) x

Model 'ar1c'.

Properties:

doc =

Auto-regressive model of order 1 w/covariates

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

pdf =

ar1c

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

14101

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

14102

name =

logit lag one correlation

short.name =

rho

prior =

pc.cor0

param =

0.5 0.5

initial =

2

fixed =

FALSE

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'ar'.

Properties:

doc =

Auto-regressive model of order p (AR(p))

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

missing.values.warning =

TRUE

pdf =

ar

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

15001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

pc.prec

param =

3 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

15002

name =

pacf1

short.name =

pacf1

initial =

1

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.5

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta3'

hyperid =

15003

name =

pacf2

short.name =

pacf2

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.4

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta4'

hyperid =

15004

name =

pacf3

short.name =

pacf3

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.3

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta5'

hyperid =

15005

name =

pacf4

short.name =

pacf4

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.2

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

15006

name =

pacf5

short.name =

pacf5

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

15007

name =

pacf6

short.name =

pacf6

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

15008

name =

pacf7

short.name =

pacf7

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

15009

name =

pacf8

short.name =

pacf8

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

15010

name =

pacf9

short.name =

pacf9

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta11'

hyperid =

15011

name =

pacf10

short.name =

pacf10

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'ou'.

Properties:

doc =

The Ornstein-Uhlenbeck process

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

ou

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

16001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

16002

name =

log phi

short.name =

phi

prior =

normal

param =

0 0.2

initial =

-1

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'intslope'.

Properties:

doc =

Intecept-slope model with Wishart-prior

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

pdf =

intslope

Number of hyperparmeters is 53.

Hyperparameter 'theta1'

hyperid =

16101

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart2d

param =

4 1 1 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

16102

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

16103

name =

logit correlation

short.name =

cor

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta4'

hyperid =

16104

name =

gamma1

short.name =

g1

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

16105

name =

gamma2

short.name =

g2

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

16106

name =

gamma3

short.name =

g3

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

16107

name =

gamma4

short.name =

g4

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

16108

name =

gamma5

short.name =

g5

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

16109

name =

gamma6

short.name =

g6

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

16110

name =

gamma7

short.name =

g7

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

16111

name =

gamma8

short.name =

g8

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

16112

name =

gamma9

short.name =

g9

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

16113

name =

gamma10

short.name =

g10

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

16114

name =

gamma11

short.name =

g11

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

16115

name =

gamma12

short.name =

g12

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

16116

name =

gamma13

short.name =

g13

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

16117

name =

gamma14

short.name =

g14

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

16118

name =

gamma15

short.name =

g15

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

16119

name =

gamma16

short.name =

g16

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

16120

name =

gamma17

short.name =

g17

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

16121

name =

gamma18

short.name =

g18

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

16122

name =

gamma19

short.name =

g19

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

16123

name =

gamma20

short.name =

g20

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

16124

name =

gamma21

short.name =

g21

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

16125

name =

gamma22

short.name =

g22

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

16126

name =

gamma23

short.name =

g23

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

16127

name =

gamma24

short.name =

g24

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

16128

name =

gamma25

short.name =

g25

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

16129

name =

gamma26

short.name =

g26

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

16130

name =

gamma27

short.name =

g27

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

16131

name =

gamma28

short.name =

g28

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

16132

name =

gamma29

short.name =

g29

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

16133

name =

gamma30

short.name =

g30

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

16134

name =

gamma31

short.name =

g31

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

16135

name =

gamma32

short.name =

g32

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

16136

name =

gamma33

short.name =

g33

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

16137

name =

gamma34

short.name =

g34

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

16138

name =

gamma35

short.name =

g35

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

16139

name =

gamma36

short.name =

g36

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

16140

name =

gamma37

short.name =

g37

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

16141

name =

gamma38

short.name =

g38

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

16142

name =

gamma39

short.name =

g39

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

16143

name =

gamma40

short.name =

g40

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

16144

name =

gamma41

short.name =

g41

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

16145

name =

gamma42

short.name =

g42

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

16146

name =

gamma43

short.name =

g43

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

16147

name =

gamma44

short.name =

g44

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

16148

name =

gamma45

short.name =

g45

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

16149

name =

gamma46

short.name =

g46

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

16150

name =

gamma47

short.name =

g47

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

16151

name =

gamma48

short.name =

g48

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

16152

name =

gamma49

short.name =

g49

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

16153

name =

gamma50

short.name =

g50

initial =

1

fixed =

TRUE

prior =

normal

param =

1 36

to.theta =

function(x) x

from.theta =

function(x) x

Model 'generic'.

Properties:

doc =

A generic model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

generic0

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

17001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'generic0'.

Properties:

doc =

A generic model (type 0)

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

generic0

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

18001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'generic1'.

Properties:

doc =

A generic model (type 1)

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

generic1

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

19001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

19002

name =

beta

short.name =

beta

initial =

2

fixed =

FALSE

prior =

gaussian

param =

0 0.1

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'generic2'.

Properties:

doc =

A generic model (type 2)

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

2

aug.constr =

2

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

generic2

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

20001

name =

log precision cmatrix

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

20002

name =

log precision random

short.name =

prec.random

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 0.001

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'generic3'.

Properties:

doc =

A generic model (type 3)

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

generic3

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

21001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

21002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

21003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

21004

name =

log precision4

short.name =

prec4

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

21005

name =

log precision5

short.name =

prec5

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

21006

name =

log precision6

short.name =

prec6

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

21007

name =

log precision7

short.name =

prec7

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

21008

name =

log precision8

short.name =

prec8

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

21009

name =

log precision9

short.name =

prec9

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

21010

name =

log precision10

short.name =

prec10

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta11'

hyperid =

21011

name =

log precision common

short.name =

prec.common

initial =

0

fixed =

TRUE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'spde'.

Properties:

doc =

A SPDE model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

spde

Number of hyperparmeters is 4.

Hyperparameter 'theta1'

hyperid =

22001

name =

theta.T

short.name =

T

initial =

2

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

22002

name =

theta.K

short.name =

K

initial =

-2

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

22003

name =

theta.KT

short.name =

KT

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

22004

name =

theta.OC

short.name =

OC

initial =

-20

fixed =

TRUE

prior =

normal

param =

0 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'spde2'.

Properties:

doc =

A SPDE2 model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

spde2

Number of hyperparmeters is 100.

Hyperparameter 'theta1'

hyperid =

23001

name =

theta1

short.name =

t1

initial =

0

fixed =

FALSE

prior =

mvnorm

param =

1 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

23002

name =

theta2

short.name =

t2

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

23003

name =

theta3

short.name =

t3

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

23004

name =

theta4

short.name =

t4

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

23005

name =

theta5

short.name =

t5

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

23006

name =

theta6

short.name =

t6

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

23007

name =

theta7

short.name =

t7

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

23008

name =

theta8

short.name =

t8

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

23009

name =

theta9

short.name =

t9

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

23010

name =

theta10

short.name =

t10

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

23011

name =

theta11

short.name =

t11

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

23012

name =

theta12

short.name =

t12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

23013

name =

theta13

short.name =

t13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

23014

name =

theta14

short.name =

t14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

23015

name =

theta15

short.name =

t15

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

23016

name =

theta16

short.name =

t16

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

23017

name =

theta17

short.name =

t17

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

23018

name =

theta18

short.name =

t18

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

23019

name =

theta19

short.name =

t19

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

23020

name =

theta20

short.name =

t20

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

23021

name =

theta21

short.name =

t21

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

23022

name =

theta22

short.name =

t22

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

23023

name =

theta23

short.name =

t23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

23024

name =

theta24

short.name =

t24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

23025

name =

theta25

short.name =

t25

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

23026

name =

theta26

short.name =

t26

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

23027

name =

theta27

short.name =

t27

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

23028

name =

theta28

short.name =

t28

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

23029

name =

theta29

short.name =

t29

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

23030

name =

theta30

short.name =

t30

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

23031

name =

theta31

short.name =

t31

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

23032

name =

theta32

short.name =

t32

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

23033

name =

theta33

short.name =

t33

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

23034

name =

theta34

short.name =

t34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

23035

name =

theta35

short.name =

t35

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

23036

name =

theta36

short.name =

t36

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

23037

name =

theta37

short.name =

t37

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

23038

name =

theta38

short.name =

t38

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

23039

name =

theta39

short.name =

t39

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

23040

name =

theta40

short.name =

t40

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

23041

name =

theta41

short.name =

t41

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

23042

name =

theta42

short.name =

t42

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

23043

name =

theta43

short.name =

t43

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

23044

name =

theta44

short.name =

t44

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

23045

name =

theta45

short.name =

t45

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

23046

name =

theta46

short.name =

t46

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

23047

name =

theta47

short.name =

t47

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

23048

name =

theta48

short.name =

t48

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

23049

name =

theta49

short.name =

t49

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

23050

name =

theta50

short.name =

t50

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

23051

name =

theta51

short.name =

t51

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

23052

name =

theta52

short.name =

t52

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

23053

name =

theta53

short.name =

t53

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

23054

name =

theta54

short.name =

t54

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

23055

name =

theta55

short.name =

t55

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

23056

name =

theta56

short.name =

t56

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

23057

name =

theta57

short.name =

t57

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

23058

name =

theta58

short.name =

t58

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

23059

name =

theta59

short.name =

t59

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

23060

name =

theta60

short.name =

t60

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

23061

name =

theta61

short.name =

t61

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

23062

name =

theta62

short.name =

t62

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

23063

name =

theta63

short.name =

t63

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

23064

name =

theta64

short.name =

t64

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

23065

name =

theta65

short.name =

t65

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

23066

name =

theta66

short.name =

t66

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

23067

name =

theta67

short.name =

t67

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

23068

name =

theta68

short.name =

t68

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

23069

name =

theta69

short.name =

t69

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

23070

name =

theta70

short.name =

t70

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

23071

name =

theta71

short.name =

t71

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

23072

name =

theta72

short.name =

t72

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

23073

name =

theta73

short.name =

t73

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

23074

name =

theta74

short.name =

t74

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

23075

name =

theta75

short.name =

t75

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

23076

name =

theta76

short.name =

t76

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

23077

name =

theta77

short.name =

t77

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

23078

name =

theta78

short.name =

t78

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

23079

name =

theta79

short.name =

t79

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

23080

name =

theta80

short.name =

t80

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

23081

name =

theta81

short.name =

t81

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

23082

name =

theta82

short.name =

t82

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

23083

name =

theta83

short.name =

t83

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

23084

name =

theta84

short.name =

t84

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

23085

name =

theta85

short.name =

t85

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

23086

name =

theta86

short.name =

t86

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

23087

name =

theta87

short.name =

t87

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

23088

name =

theta88

short.name =

t88

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

23089

name =

theta89

short.name =

t89

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

23090

name =

theta90

short.name =

t90

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

23091

name =

theta91

short.name =

t91

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

23092

name =

theta92

short.name =

t92

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

23093

name =

theta93

short.name =

t93

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

23094

name =

theta94

short.name =

t94

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

23095

name =

theta95

short.name =

t95

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

23096

name =

theta96

short.name =

t96

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

23097

name =

theta97

short.name =

t97

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

23098

name =

theta98

short.name =

t98

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

23099

name =

theta99

short.name =

t99

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

23100

name =

theta100

short.name =

t100

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'spde3'.

Properties:

doc =

A SPDE3 model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

spde3

Number of hyperparmeters is 100.

Hyperparameter 'theta1'

hyperid =

24001

name =

theta1

short.name =

t1

initial =

0

fixed =

FALSE

prior =

mvnorm

param =

1 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

24002

name =

theta2

short.name =

t2

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

24003

name =

theta3

short.name =

t3

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

24004

name =

theta4

short.name =

t4

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

24005

name =

theta5

short.name =

t5

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

24006

name =

theta6

short.name =

t6

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

24007

name =

theta7

short.name =

t7

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

24008

name =

theta8

short.name =

t8

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

24009

name =

theta9

short.name =

t9

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

24010

name =

theta10

short.name =

t10

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

24011

name =

theta11

short.name =

t11

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

24012

name =

theta12

short.name =

t12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

24013

name =

theta13

short.name =

t13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

24014

name =

theta14

short.name =

t14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

24015

name =

theta15

short.name =

t15

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

24016

name =

theta16

short.name =

t16

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

24017

name =

theta17

short.name =

t17

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

24018

name =

theta18

short.name =

t18

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

24019

name =

theta19

short.name =

t19

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

24020

name =

theta20

short.name =

t20

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

24021

name =

theta21

short.name =

t21

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

24022

name =

theta22

short.name =

t22

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

24023

name =

theta23

short.name =

t23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

24024

name =

theta24

short.name =

t24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

24025

name =

theta25

short.name =

t25

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

24026

name =

theta26

short.name =

t26

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

24027

name =

theta27

short.name =

t27

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

24028

name =

theta28

short.name =

t28

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

24029

name =

theta29

short.name =

t29

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

24030

name =

theta30

short.name =

t30

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

24031

name =

theta31

short.name =

t31

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

24032

name =

theta32

short.name =

t32

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

24033

name =

theta33

short.name =

t33

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

24034

name =

theta34

short.name =

t34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

24035

name =

theta35

short.name =

t35

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

24036

name =

theta36

short.name =

t36

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

24037

name =

theta37

short.name =

t37

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

24038

name =

theta38

short.name =

t38

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

24039

name =

theta39

short.name =

t39

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

24040

name =

theta40

short.name =

t40

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

24041

name =

theta41

short.name =

t41

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

24042

name =

theta42

short.name =

t42

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

24043

name =

theta43

short.name =

t43

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

24044

name =

theta44

short.name =

t44

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

24045

name =

theta45

short.name =

t45

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

24046

name =

theta46

short.name =

t46

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

24047

name =

theta47

short.name =

t47

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

24048

name =

theta48

short.name =

t48

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

24049

name =

theta49

short.name =

t49

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

24050

name =

theta50

short.name =

t50

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

24051

name =

theta51

short.name =

t51

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

24052

name =

theta52

short.name =

t52

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

24053

name =

theta53

short.name =

t53

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

24054

name =

theta54

short.name =

t54

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

24055

name =

theta55

short.name =

t55

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

24056

name =

theta56

short.name =

t56

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

24057

name =

theta57

short.name =

t57

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

24058

name =

theta58

short.name =

t58

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

24059

name =

theta59

short.name =

t59

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

24060

name =

theta60

short.name =

t60

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

24061

name =

theta61

short.name =

t61

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

24062

name =

theta62

short.name =

t62

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

24063

name =

theta63

short.name =

t63

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

24064

name =

theta64

short.name =

t64

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

24065

name =

theta65

short.name =

t65

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

24066

name =

theta66

short.name =

t66

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

24067

name =

theta67

short.name =

t67

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

24068

name =

theta68

short.name =

t68

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

24069

name =

theta69

short.name =

t69

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

24070

name =

theta70

short.name =

t70

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

24071

name =

theta71

short.name =

t71

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

24072

name =

theta72

short.name =

t72

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

24073

name =

theta73

short.name =

t73

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

24074

name =

theta74

short.name =

t74

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

24075

name =

theta75

short.name =

t75

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

24076

name =

theta76

short.name =

t76

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

24077

name =

theta77

short.name =

t77

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

24078

name =

theta78

short.name =

t78

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

24079

name =

theta79

short.name =

t79

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

24080

name =

theta80

short.name =

t80

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

24081

name =

theta81

short.name =

t81

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

24082

name =

theta82

short.name =

t82

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

24083

name =

theta83

short.name =

t83

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

24084

name =

theta84

short.name =

t84

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

24085

name =

theta85

short.name =

t85

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

24086

name =

theta86

short.name =

t86

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

24087

name =

theta87

short.name =

t87

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

24088

name =

theta88

short.name =

t88

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

24089

name =

theta89

short.name =

t89

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

24090

name =

theta90

short.name =

t90

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

24091

name =

theta91

short.name =

t91

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

24092

name =

theta92

short.name =

t92

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

24093

name =

theta93

short.name =

t93

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

24094

name =

theta94

short.name =

t94

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

24095

name =

theta95

short.name =

t95

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

24096

name =

theta96

short.name =

t96

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

24097

name =

theta97

short.name =

t97

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

24098

name =

theta98

short.name =

t98

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

24099

name =

theta99

short.name =

t99

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

24100

name =

theta100

short.name =

t100

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'iid1d'.

Properties:

doc =

Gaussian random effect in dim=1 with Wishart prior

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

pdf =

iid123d

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

25001

name =

precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

wishart1d

param =

2 1e-04

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iid2d'.

Properties:

doc =

Gaussian random effect in dim=2 with Wishart prior

constr =

FALSE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

1

aug.constr =

1 2

n.div.by =

2

n.required =

TRUE

set.default.values =

TRUE

pdf =

iid123d

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

26001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart2d

param =

4 1 1 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

26002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

26003

name =

logit correlation

short.name =

cor

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iid3d'.

Properties:

doc =

Gaussian random effect in dim=3 with Wishart prior

constr =

FALSE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

1

aug.constr =

1 2 3

n.div.by =

3

n.required =

TRUE

set.default.values =

TRUE

pdf =

iid123d

Number of hyperparmeters is 6.

Hyperparameter 'theta1'

hyperid =

27001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart3d

param =

7 1 1 1 0 0 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

27002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

27003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

27004

name =

logit correlation12

short.name =

cor12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta5'

hyperid =

27005

name =

logit correlation13

short.name =

cor13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

27006

name =

logit correlation23

short.name =

cor23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iid4d'.

Properties:

doc =

Gaussian random effect in dim=4 with Wishart prior

constr =

FALSE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

1

aug.constr =

1 2 3 4

n.div.by =

4

n.required =

TRUE

set.default.values =

TRUE

pdf =

iid123d

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

28001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart4d

param =

11 1 1 1 1 0 0 0 0 0 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

28002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

28003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

28004

name =

log precision4

short.name =

prec4

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

28005

name =

logit correlation12

short.name =

cor12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

28006

name =

logit correlation13

short.name =

cor13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

28007

name =

logit correlation14

short.name =

cor14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

28008

name =

logit correlation23

short.name =

cor23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

28009

name =

logit correlation24

short.name =

cor24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

28010

name =

logit correlation34

short.name =

cor34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iid5d'.

Properties:

doc =

Gaussian random effect in dim=5 with Wishart prior

constr =

FALSE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

1

aug.constr =

1 2 3 4 5

n.div.by =

5

n.required =

TRUE

set.default.values =

TRUE

pdf =

iid123d

Number of hyperparmeters is 15.

Hyperparameter 'theta1'

hyperid =

29001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

wishart5d

param =

16 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

29002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

29003

name =

log precision3

short.name =

prec3

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

29004

name =

log precision4

short.name =

prec4

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

29005

name =

log precision5

short.name =

prec5

initial =

4

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

29006

name =

logit correlation12

short.name =

cor12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

29007

name =

logit correlation13

short.name =

cor13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

29008

name =

logit correlation14

short.name =

cor14

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

29009

name =

logit correlation15

short.name =

cor15

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

29010

name =

logit correlation23

short.name =

cor23

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta11'

hyperid =

29011

name =

logit correlation24

short.name =

cor24

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta12'

hyperid =

29012

name =

logit correlation25

short.name =

cor25

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta13'

hyperid =

29013

name =

logit correlation34

short.name =

cor34

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta14'

hyperid =

29014

name =

logit correlation35

short.name =

cor35

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta15'

hyperid =

29015

name =

logit correlation45

short.name =

cor45

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'iidkd'.

Properties:

doc =

Gaussian random effect in dim=k with Wishart prior

constr =

FALSE

nrow.ncol =

FALSE

augmented =

TRUE

aug.factor =

1

aug.constr =

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

n.div.by =

-1

n.required =

TRUE

set.default.values =

TRUE

pdf =

iidkd

Number of hyperparmeters is 300.

Hyperparameter 'theta1'

hyperid =

29101

name =

theta1

short.name =

theta1

initial =

1048576

fixed =

FALSE

prior =

wishartkd

param =

30 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576 1048576

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

29102

name =

theta2

short.name =

theta2

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

29103

name =

theta3

short.name =

theta3

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

29104

name =

theta4

short.name =

theta4

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

29105

name =

theta5

short.name =

theta5

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

29106

name =

theta6

short.name =

theta6

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

29107

name =

theta7

short.name =

theta7

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

29108

name =

theta8

short.name =

theta8

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

29109

name =

theta9

short.name =

theta9

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

29110

name =

theta10

short.name =

theta10

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

29111

name =

theta11

short.name =

theta11

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

29112

name =

theta12

short.name =

theta12

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

29113

name =

theta13

short.name =

theta13

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

29114

name =

theta14

short.name =

theta14

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

29115

name =

theta15

short.name =

theta15

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

29116

name =

theta16

short.name =

theta16

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

29117

name =

theta17

short.name =

theta17

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

29118

name =

theta18

short.name =

theta18

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

29119

name =

theta19

short.name =

theta19

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

29120

name =

theta20

short.name =

theta20

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

29121

name =

theta21

short.name =

theta21

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

29122

name =

theta22

short.name =

theta22

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

29123

name =

theta23

short.name =

theta23

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

29124

name =

theta24

short.name =

theta24

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

29125

name =

theta25

short.name =

theta25

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

29126

name =

theta26

short.name =

theta26

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

29127

name =

theta27

short.name =

theta27

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

29128

name =

theta28

short.name =

theta28

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

29129

name =

theta29

short.name =

theta29

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

29130

name =

theta30

short.name =

theta30

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

29131

name =

theta31

short.name =

theta31

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

29132

name =

theta32

short.name =

theta32

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

29133

name =

theta33

short.name =

theta33

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

29134

name =

theta34

short.name =

theta34

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

29135

name =

theta35

short.name =

theta35

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

29136

name =

theta36

short.name =

theta36

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

29137

name =

theta37

short.name =

theta37

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

29138

name =

theta38

short.name =

theta38

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

29139

name =

theta39

short.name =

theta39

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

29140

name =

theta40

short.name =

theta40

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

29141

name =

theta41

short.name =

theta41

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

29142

name =

theta42

short.name =

theta42

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

29143

name =

theta43

short.name =

theta43

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

29144

name =

theta44

short.name =

theta44

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

29145

name =

theta45

short.name =

theta45

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

29146

name =

theta46

short.name =

theta46

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

29147

name =

theta47

short.name =

theta47

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

29148

name =

theta48

short.name =

theta48

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

29149

name =

theta49

short.name =

theta49

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

29150

name =

theta50

short.name =

theta50

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

29151

name =

theta51

short.name =

theta51

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

29152

name =

theta52

short.name =

theta52

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

29153

name =

theta53

short.name =

theta53

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

29154

name =

theta54

short.name =

theta54

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

29155

name =

theta55

short.name =

theta55

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

29156

name =

theta56

short.name =

theta56

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

29157

name =

theta57

short.name =

theta57

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

29158

name =

theta58

short.name =

theta58

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

29159

name =

theta59

short.name =

theta59

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

29160

name =

theta60

short.name =

theta60

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

29161

name =

theta61

short.name =

theta61

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

29162

name =

theta62

short.name =

theta62

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

29163

name =

theta63

short.name =

theta63

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

29164

name =

theta64

short.name =

theta64

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

29165

name =

theta65

short.name =

theta65

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

29166

name =

theta66

short.name =

theta66

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

29167

name =

theta67

short.name =

theta67

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

29168

name =

theta68

short.name =

theta68

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

29169

name =

theta69

short.name =

theta69

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

29170

name =

theta70

short.name =

theta70

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

29171

name =

theta71

short.name =

theta71

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

29172

name =

theta72

short.name =

theta72

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

29173

name =

theta73

short.name =

theta73

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

29174

name =

theta74

short.name =

theta74

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

29175

name =

theta75

short.name =

theta75

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

29176

name =

theta76

short.name =

theta76

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

29177

name =

theta77

short.name =

theta77

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

29178

name =

theta78

short.name =

theta78

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

29179

name =

theta79

short.name =

theta79

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

29180

name =

theta80

short.name =

theta80

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

29181

name =

theta81

short.name =

theta81

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

29182

name =

theta82

short.name =

theta82

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

29183

name =

theta83

short.name =

theta83

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

29184

name =

theta84

short.name =

theta84

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

29185

name =

theta85

short.name =

theta85

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

29186

name =

theta86

short.name =

theta86

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

29187

name =

theta87

short.name =

theta87

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

29188

name =

theta88

short.name =

theta88

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

29189

name =

theta89

short.name =

theta89

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

29190

name =

theta90

short.name =

theta90

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

29191

name =

theta91

short.name =

theta91

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

29192

name =

theta92

short.name =

theta92

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

29193

name =

theta93

short.name =

theta93

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

29194

name =

theta94

short.name =

theta94

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

29195

name =

theta95

short.name =

theta95

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

29196

name =

theta96

short.name =

theta96

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

29197

name =

theta97

short.name =

theta97

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

29198

name =

theta98

short.name =

theta98

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

29199

name =

theta99

short.name =

theta99

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

29200

name =

theta100

short.name =

theta100

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta101'

hyperid =

29201

name =

theta101

short.name =

theta101

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta102'

hyperid =

29202

name =

theta102

short.name =

theta102

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta103'

hyperid =

29203

name =

theta103

short.name =

theta103

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta104'

hyperid =

29204

name =

theta104

short.name =

theta104

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta105'

hyperid =

29205

name =

theta105

short.name =

theta105

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta106'

hyperid =

29206

name =

theta106

short.name =

theta106

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta107'

hyperid =

29207

name =

theta107

short.name =

theta107

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta108'

hyperid =

29208

name =

theta108

short.name =

theta108

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta109'

hyperid =

29209

name =

theta109

short.name =

theta109

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta110'

hyperid =

29210

name =

theta110

short.name =

theta110

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta111'

hyperid =

29211

name =

theta111

short.name =

theta111

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta112'

hyperid =

29212

name =

theta112

short.name =

theta112

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta113'

hyperid =

29213

name =

theta113

short.name =

theta113

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta114'

hyperid =

29214

name =

theta114

short.name =

theta114

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta115'

hyperid =

29215

name =

theta115

short.name =

theta115

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta116'

hyperid =

29216

name =

theta116

short.name =

theta116

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta117'

hyperid =

29217

name =

theta117

short.name =

theta117

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta118'

hyperid =

29218

name =

theta118

short.name =

theta118

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta119'

hyperid =

29219

name =

theta119

short.name =

theta119

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta120'

hyperid =

29220

name =

theta120

short.name =

theta120

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta121'

hyperid =

29221

name =

theta121

short.name =

theta121

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta122'

hyperid =

29222

name =

theta122

short.name =

theta122

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta123'

hyperid =

29223

name =

theta123

short.name =

theta123

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta124'

hyperid =

29224

name =

theta124

short.name =

theta124

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta125'

hyperid =

29225

name =

theta125

short.name =

theta125

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta126'

hyperid =

29226

name =

theta126

short.name =

theta126

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta127'

hyperid =

29227

name =

theta127

short.name =

theta127

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta128'

hyperid =

29228

name =

theta128

short.name =

theta128

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta129'

hyperid =

29229

name =

theta129

short.name =

theta129

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta130'

hyperid =

29230

name =

theta130

short.name =

theta130

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta131'

hyperid =

29231

name =

theta131

short.name =

theta131

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta132'

hyperid =

29232

name =

theta132

short.name =

theta132

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta133'

hyperid =

29233

name =

theta133

short.name =

theta133

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta134'

hyperid =

29234

name =

theta134

short.name =

theta134

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta135'

hyperid =

29235

name =

theta135

short.name =

theta135

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta136'

hyperid =

29236

name =

theta136

short.name =

theta136

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta137'

hyperid =

29237

name =

theta137

short.name =

theta137

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta138'

hyperid =

29238

name =

theta138

short.name =

theta138

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta139'

hyperid =

29239

name =

theta139

short.name =

theta139

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta140'

hyperid =

29240

name =

theta140

short.name =

theta140

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta141'

hyperid =

29241

name =

theta141

short.name =

theta141

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta142'

hyperid =

29242

name =

theta142

short.name =

theta142

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta143'

hyperid =

29243

name =

theta143

short.name =

theta143

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta144'

hyperid =

29244

name =

theta144

short.name =

theta144

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta145'

hyperid =

29245

name =

theta145

short.name =

theta145

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta146'

hyperid =

29246

name =

theta146

short.name =

theta146

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta147'

hyperid =

29247

name =

theta147

short.name =

theta147

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta148'

hyperid =

29248

name =

theta148

short.name =

theta148

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta149'

hyperid =

29249

name =

theta149

short.name =

theta149

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta150'

hyperid =

29250

name =

theta150

short.name =

theta150

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta151'

hyperid =

29251

name =

theta151

short.name =

theta151

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta152'

hyperid =

29252

name =

theta152

short.name =

theta152

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta153'

hyperid =

29253

name =

theta153

short.name =

theta153

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta154'

hyperid =

29254

name =

theta154

short.name =

theta154

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta155'

hyperid =

29255

name =

theta155

short.name =

theta155

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta156'

hyperid =

29256

name =

theta156

short.name =

theta156

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta157'

hyperid =

29257

name =

theta157

short.name =

theta157

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta158'

hyperid =

29258

name =

theta158

short.name =

theta158

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta159'

hyperid =

29259

name =

theta159

short.name =

theta159

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta160'

hyperid =

29260

name =

theta160

short.name =

theta160

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta161'

hyperid =

29261

name =

theta161

short.name =

theta161

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta162'

hyperid =

29262

name =

theta162

short.name =

theta162

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta163'

hyperid =

29263

name =

theta163

short.name =

theta163

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta164'

hyperid =

29264

name =

theta164

short.name =

theta164

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta165'

hyperid =

29265

name =

theta165

short.name =

theta165

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta166'

hyperid =

29266

name =

theta166

short.name =

theta166

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta167'

hyperid =

29267

name =

theta167

short.name =

theta167

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta168'

hyperid =

29268

name =

theta168

short.name =

theta168

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta169'

hyperid =

29269

name =

theta169

short.name =

theta169

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta170'

hyperid =

29270

name =

theta170

short.name =

theta170

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta171'

hyperid =

29271

name =

theta171

short.name =

theta171

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta172'

hyperid =

29272

name =

theta172

short.name =

theta172

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta173'

hyperid =

29273

name =

theta173

short.name =

theta173

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta174'

hyperid =

29274

name =

theta174

short.name =

theta174

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta175'

hyperid =

29275

name =

theta175

short.name =

theta175

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta176'

hyperid =

29276

name =

theta176

short.name =

theta176

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta177'

hyperid =

29277

name =

theta177

short.name =

theta177

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta178'

hyperid =

29278

name =

theta178

short.name =

theta178

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta179'

hyperid =

29279

name =

theta179

short.name =

theta179

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta180'

hyperid =

29280

name =

theta180

short.name =

theta180

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta181'

hyperid =

29281

name =

theta181

short.name =

theta181

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta182'

hyperid =

29282

name =

theta182

short.name =

theta182

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta183'

hyperid =

29283

name =

theta183

short.name =

theta183

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta184'

hyperid =

29284

name =

theta184

short.name =

theta184

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta185'

hyperid =

29285

name =

theta185

short.name =

theta185

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta186'

hyperid =

29286

name =

theta186

short.name =

theta186

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta187'

hyperid =

29287

name =

theta187

short.name =

theta187

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta188'

hyperid =

29288

name =

theta188

short.name =

theta188

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta189'

hyperid =

29289

name =

theta189

short.name =

theta189

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta190'

hyperid =

29290

name =

theta190

short.name =

theta190

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta191'

hyperid =

29291

name =

theta191

short.name =

theta191

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta192'

hyperid =

29292

name =

theta192

short.name =

theta192

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta193'

hyperid =

29293

name =

theta193

short.name =

theta193

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta194'

hyperid =

29294

name =

theta194

short.name =

theta194

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta195'

hyperid =

29295

name =

theta195

short.name =

theta195

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta196'

hyperid =

29296

name =

theta196

short.name =

theta196

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta197'

hyperid =

29297

name =

theta197

short.name =

theta197

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta198'

hyperid =

29298

name =

theta198

short.name =

theta198

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta199'

hyperid =

29299

name =

theta199

short.name =

theta199

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta200'

hyperid =

29300

name =

theta200

short.name =

theta200

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta201'

hyperid =

29301

name =

theta201

short.name =

theta201

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta202'

hyperid =

29302

name =

theta202

short.name =

theta202

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta203'

hyperid =

29303

name =

theta203

short.name =

theta203

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta204'

hyperid =

29304

name =

theta204

short.name =

theta204

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta205'

hyperid =

29305

name =

theta205

short.name =

theta205

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta206'

hyperid =

29306

name =

theta206

short.name =

theta206

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta207'

hyperid =

29307

name =

theta207

short.name =

theta207

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta208'

hyperid =

29308

name =

theta208

short.name =

theta208

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta209'

hyperid =

29309

name =

theta209

short.name =

theta209

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta210'

hyperid =

29310

name =

theta210

short.name =

theta210

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta211'

hyperid =

29311

name =

theta211

short.name =

theta211

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta212'

hyperid =

29312

name =

theta212

short.name =

theta212

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta213'

hyperid =

29313

name =

theta213

short.name =

theta213

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta214'

hyperid =

29314

name =

theta214

short.name =

theta214

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta215'

hyperid =

29315

name =

theta215

short.name =

theta215

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta216'

hyperid =

29316

name =

theta216

short.name =

theta216

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta217'

hyperid =

29317

name =

theta217

short.name =

theta217

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta218'

hyperid =

29318

name =

theta218

short.name =

theta218

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta219'

hyperid =

29319

name =

theta219

short.name =

theta219

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta220'

hyperid =

29320

name =

theta220

short.name =

theta220

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta221'

hyperid =

29321

name =

theta221

short.name =

theta221

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta222'

hyperid =

29322

name =

theta222

short.name =

theta222

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta223'

hyperid =

29323

name =

theta223

short.name =

theta223

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta224'

hyperid =

29324

name =

theta224

short.name =

theta224

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta225'

hyperid =

29325

name =

theta225

short.name =

theta225

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta226'

hyperid =

29326

name =

theta226

short.name =

theta226

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta227'

hyperid =

29327

name =

theta227

short.name =

theta227

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta228'

hyperid =

29328

name =

theta228

short.name =

theta228

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta229'

hyperid =

29329

name =

theta229

short.name =

theta229

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta230'

hyperid =

29330

name =

theta230

short.name =

theta230

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta231'

hyperid =

29331

name =

theta231

short.name =

theta231

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta232'

hyperid =

29332

name =

theta232

short.name =

theta232

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta233'

hyperid =

29333

name =

theta233

short.name =

theta233

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta234'

hyperid =

29334

name =

theta234

short.name =

theta234

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta235'

hyperid =

29335

name =

theta235

short.name =

theta235

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta236'

hyperid =

29336

name =

theta236

short.name =

theta236

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta237'

hyperid =

29337

name =

theta237

short.name =

theta237

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta238'

hyperid =

29338

name =

theta238

short.name =

theta238

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta239'

hyperid =

29339

name =

theta239

short.name =

theta239

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta240'

hyperid =

29340

name =

theta240

short.name =

theta240

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta241'

hyperid =

29341

name =

theta241

short.name =

theta241

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta242'

hyperid =

29342

name =

theta242

short.name =

theta242

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta243'

hyperid =

29343

name =

theta243

short.name =

theta243

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta244'

hyperid =

29344

name =

theta244

short.name =

theta244

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta245'

hyperid =

29345

name =

theta245

short.name =

theta245

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta246'

hyperid =

29346

name =

theta246

short.name =

theta246

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta247'

hyperid =

29347

name =

theta247

short.name =

theta247

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta248'

hyperid =

29348

name =

theta248

short.name =

theta248

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta249'

hyperid =

29349

name =

theta249

short.name =

theta249

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta250'

hyperid =

29350

name =

theta250

short.name =

theta250

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta251'

hyperid =

29351

name =

theta251

short.name =

theta251

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta252'

hyperid =

29352

name =

theta252

short.name =

theta252

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta253'

hyperid =

29353

name =

theta253

short.name =

theta253

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta254'

hyperid =

29354

name =

theta254

short.name =

theta254

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta255'

hyperid =

29355

name =

theta255

short.name =

theta255

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta256'

hyperid =

29356

name =

theta256

short.name =

theta256

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta257'

hyperid =

29357

name =

theta257

short.name =

theta257

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta258'

hyperid =

29358

name =

theta258

short.name =

theta258

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta259'

hyperid =

29359

name =

theta259

short.name =

theta259

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta260'

hyperid =

29360

name =

theta260

short.name =

theta260

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta261'

hyperid =

29361

name =

theta261

short.name =

theta261

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta262'

hyperid =

29362

name =

theta262

short.name =

theta262

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta263'

hyperid =

29363

name =

theta263

short.name =

theta263

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta264'

hyperid =

29364

name =

theta264

short.name =

theta264

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta265'

hyperid =

29365

name =

theta265

short.name =

theta265

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta266'

hyperid =

29366

name =

theta266

short.name =

theta266

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta267'

hyperid =

29367

name =

theta267

short.name =

theta267

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta268'

hyperid =

29368

name =

theta268

short.name =

theta268

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta269'

hyperid =

29369

name =

theta269

short.name =

theta269

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta270'

hyperid =

29370

name =

theta270

short.name =

theta270

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta271'

hyperid =

29371

name =

theta271

short.name =

theta271

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta272'

hyperid =

29372

name =

theta272

short.name =

theta272

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta273'

hyperid =

29373

name =

theta273

short.name =

theta273

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta274'

hyperid =

29374

name =

theta274

short.name =

theta274

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta275'

hyperid =

29375

name =

theta275

short.name =

theta275

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta276'

hyperid =

29376

name =

theta276

short.name =

theta276

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta277'

hyperid =

29377

name =

theta277

short.name =

theta277

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta278'

hyperid =

29378

name =

theta278

short.name =

theta278

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta279'

hyperid =

29379

name =

theta279

short.name =

theta279

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta280'

hyperid =

29380

name =

theta280

short.name =

theta280

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta281'

hyperid =

29381

name =

theta281

short.name =

theta281

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta282'

hyperid =

29382

name =

theta282

short.name =

theta282

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta283'

hyperid =

29383

name =

theta283

short.name =

theta283

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta284'

hyperid =

29384

name =

theta284

short.name =

theta284

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta285'

hyperid =

29385

name =

theta285

short.name =

theta285

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta286'

hyperid =

29386

name =

theta286

short.name =

theta286

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta287'

hyperid =

29387

name =

theta287

short.name =

theta287

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta288'

hyperid =

29388

name =

theta288

short.name =

theta288

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta289'

hyperid =

29389

name =

theta289

short.name =

theta289

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta290'

hyperid =

29390

name =

theta290

short.name =

theta290

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta291'

hyperid =

29391

name =

theta291

short.name =

theta291

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta292'

hyperid =

29392

name =

theta292

short.name =

theta292

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta293'

hyperid =

29393

name =

theta293

short.name =

theta293

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta294'

hyperid =

29394

name =

theta294

short.name =

theta294

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta295'

hyperid =

29395

name =

theta295

short.name =

theta295

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta296'

hyperid =

29396

name =

theta296

short.name =

theta296

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta297'

hyperid =

29397

name =

theta297

short.name =

theta297

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta298'

hyperid =

29398

name =

theta298

short.name =

theta298

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta299'

hyperid =

29399

name =

theta299

short.name =

theta299

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta300'

hyperid =

29400

name =

theta300

short.name =

theta300

initial =

1048576

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model '2diid'.

Properties:

doc =

(This model is obsolute)

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

1 2

n.div.by =

2

n.required =

TRUE

set.default.values =

TRUE

pdf =

iid123d

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

30001

name =

log precision1

short.name =

prec1

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

30002

name =

log precision2

short.name =

prec2

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

30003

name =

correlation

short.name =

cor

initial =

4

fixed =

FALSE

prior =

normal

param =

0 0.15

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'z'.

Properties:

doc =

The z-model in a classical mixed model formulation

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

z

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

31001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2d'.

Properties:

doc =

Thin-plate spline model

constr =

TRUE

nrow.ncol =

TRUE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

pdf =

rw2d

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

32001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2diid'.

Properties:

doc =

Thin-plate spline with iid noise

constr =

TRUE

nrow.ncol =

TRUE

augmented =

TRUE

aug.factor =

2

aug.constr =

2

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

pdf =

rw2diid

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

33001

name =

log precision

short.name =

prec

prior =

pc.prec

param =

1 0.01

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

33002

name =

logit phi

short.name =

phi

prior =

pc

param =

0.5 0.5

initial =

3

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'slm'.

Properties:

doc =

Spatial lag model

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

slm

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

34001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

34002

name =

rho

short.name =

rho

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) 1 / (1 + exp(-x))

Model 'matern2d'.

Properties:

doc =

Matern covariance function on a regular grid

constr =

FALSE

nrow.ncol =

TRUE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

TRUE

pdf =

matern2d

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

35001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

35002

name =

log range

short.name =

range

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'dmatern'.

Properties:

doc =

Dense Matern field

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

TRUE

set.default.values =

TRUE

pdf =

dmatern

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

35101

name =

log precision

short.name =

prec

initial =

3

fixed =

FALSE

prior =

pc.prec

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

35102

name =

log range

short.name =

range

initial =

0

fixed =

FALSE

prior =

pc.range

param =

1 0.5

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

35103

name =

log nu

short.name =

nu

initial =

-0.693147180559945

fixed =

TRUE

prior =

loggamma

param =

0.5 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'copy'.

Properties:

doc =

Create a copy of a model component

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

copy

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

36001

name =

beta

short.name =

b

initial =

0

fixed =

TRUE

prior =

normal

param =

1 10

to.theta =

function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(log(-(low - x) / (high - x))) } else if (is.finite(low) && is.infinite(high) && high > low) { return(log(x - low)) } else { stop("Condition not yet implemented") } }

from.theta =

function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(low + exp(x) / (1 + exp(x)) * (high - low)) } else if (is.finite(low) && is.infinite(high) && high > low) { return(low + exp(x)) } else { stop("Condition not yet implemented") } }

Model 'scopy'.

Properties:

doc =

Create a scopy of a model component

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

scopy

Number of hyperparmeters is 15.

Hyperparameter 'theta1'

hyperid =

36101

name =

mean

short.name =

mean

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

36102

name =

slope

short.name =

slope

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

36103

name =

spline.theta1

short.name =

spline

initial =

0

fixed =

FALSE

prior =

laplace

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

36104

name =

spline.theta2

short.name =

spline2

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

36105

name =

spline.theta3

short.name =

spline3

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

36106

name =

spline.theta4

short.name =

spline4

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

36107

name =

spline.theta5

short.name =

spline5

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

36108

name =

spline.theta6

short.name =

spline6

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

36109

name =

spline.theta7

short.name =

spline7

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

36110

name =

spline.theta8

short.name =

spline8

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

36111

name =

spline.theta9

short.name =

spline9

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

36112

name =

spline.theta10

short.name =

spline10

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

36113

name =

spline.theta11

short.name =

spline11

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

36114

name =

spline.theta12

short.name =

spline12

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

36115

name =

spline.theta13

short.name =

spline13

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'clinear'.

Properties:

doc =

Constrained linear effect

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

clinear

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

37001

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { stopifnot(low < high) return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(log(-(low - x) / (high - x))) } else if (is.finite(low) && is.infinite(high) && high > low) { return(log(x - low)) } else { stop("Condition not yet implemented") } }

from.theta =

function(x, REPLACE.ME.low, REPLACE.ME.high) { if (all(is.infinite(c(low, high))) || low == high) { stopifnot(low < high) return(x) } else if (all(is.finite(c(low, high)))) { stopifnot(low < high) return(low + exp(x) / (1 + exp(x)) * (high - low)) } else if (is.finite(low) && is.infinite(high) && high > low) { return(low + exp(x)) } else { stop("Condition not yet implemented") } }

Model 'sigm'.

Properties:

doc =

Sigmoidal effect of a covariate

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

sigm

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

38001

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

38002

name =

loghalflife

short.name =

halflife

initial =

3

fixed =

FALSE

prior =

loggamma

param =

3 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

38003

name =

logshape

short.name =

shape

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'revsigm'.

Properties:

doc =

Reverse sigmoidal effect of a covariate

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

sigm

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

39001

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

39002

name =

loghalflife

short.name =

halflife

initial =

3

fixed =

FALSE

prior =

loggamma

param =

3 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

39003

name =

logshape

short.name =

shape

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'log1exp'.

Properties:

doc =

A nonlinear model of a covariate

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

log1exp

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

39011

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

39012

name =

alpha

short.name =

a

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

39013

name =

gamma

short.name =

g

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'logdist'.

Properties:

doc =

A nonlinear model of a covariate

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

logdist

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

39021

name =

beta

short.name =

b

initial =

1

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

39022

name =

alpha1

short.name =

a1

initial =

0

fixed =

FALSE

prior =

loggamma

param =

0.1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

39023

name =

alpha2

short.name =

a2

initial =

0

fixed =

FALSE

prior =

loggamma

param =

0.1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'group'

Valid models in this section are:

Model 'exchangeable'.

Properties:

doc =

Exchangeable correlations

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

40001

name =

logit correlation

short.name =

rho

initial =

1

fixed =

FALSE

prior =

normal

param =

0 0.2

to.theta =

function(x, REPLACE.ME.ngroup) log((1 + x * (ngroup - 1)) / (1 - x))

from.theta =

function(x, REPLACE.ME.ngroup) (exp(x) - 1) / (exp(x) + ngroup - 1)

Model 'exchangeablepos'.

Properties:

doc =

Exchangeable positive correlations

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

40101

name =

logit correlation

short.name =

rho

initial =

1

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.5

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'ar1'.

Properties:

doc =

AR(1) correlations

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

41001

name =

logit correlation

short.name =

rho

initial =

2

fixed =

FALSE

prior =

normal

param =

0 0.15

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'ar'.

Properties:

doc =

AR(p) correlations

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

42001

name =

log precision

short.name =

prec

initial =

0

fixed =

TRUE

prior =

pc.prec

param =

3 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

42002

name =

pacf1

short.name =

pacf1

initial =

2

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.5

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta3'

hyperid =

42003

name =

pacf2

short.name =

pacf2

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.4

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta4'

hyperid =

42004

name =

pacf3

short.name =

pacf3

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.3

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta5'

hyperid =

42005

name =

pacf4

short.name =

pacf4

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.2

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta6'

hyperid =

42006

name =

pacf5

short.name =

pacf5

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta7'

hyperid =

42007

name =

pacf6

short.name =

pacf6

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta8'

hyperid =

42008

name =

pacf7

short.name =

pacf7

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta9'

hyperid =

42009

name =

pacf8

short.name =

pacf8

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta10'

hyperid =

42010

name =

pacf9

short.name =

pacf9

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Hyperparameter 'theta11'

hyperid =

42011

name =

pacf10

short.name =

pacf10

initial =

0

fixed =

FALSE

prior =

pc.cor0

param =

0.5 0.1

to.theta =

function(x) log((1 + x) / (1 - x))

from.theta =

function(x) 2 * exp(x) / (1 + exp(x)) - 1

Model 'rw1'.

Properties:

doc =

Random walk of order 1

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

43001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2'.

Properties:

doc =

Random walk of order 2

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

44001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'besag'.

Properties:

doc =

Besag model

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

45001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iid'.

Properties:

doc =

Independent model

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

46001

name =

log precision

short.name =

prec

prior =

loggamma

param =

1 5e-05

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'scopy'

Valid models in this section are:

Model 'rw1'.

Properties:

doc =

Random walk of order 1

Number of hyperparmeters is 0.

Model 'rw2'.

Properties:

doc =

Random walk of order 2

Number of hyperparmeters is 0.

'mix'

Valid models in this section are:

Model 'gaussian'.

Properties:

doc =

Gaussian mixture

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

47001

name =

log precision

short.name =

prec

output.name =

Precision for the Gaussian observations

output.name.intern =

Log precision for the Gaussian observations

prior =

pc.prec

param =

1 0.01

initial =

0

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'loggamma'.

Properties:

doc =

LogGamma mixture

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

47101

name =

log precision

short.name =

prec

prior =

pc.mgamma

param =

4.8

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'mloggamma'.

Properties:

doc =

Minus-LogGamma mixture

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

47201

name =

log precision

short.name =

prec

prior =

pc.mgamma

param =

4.8

initial =

4

fixed =

FALSE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'link'

Valid models in this section are:

Model 'default'.

Properties:

doc =

The default link

Number of hyperparmeters is 0.

Model 'cloglog'.

Properties:

doc =

The complementary log-log link

Number of hyperparmeters is 0.

Model 'ccloglog'.

Properties:

doc =

The complement complementary log-log link

Number of hyperparmeters is 0.

Model 'loglog'.

Properties:

doc =

The log-log link

Number of hyperparmeters is 0.

Model 'identity'.

Properties:

doc =

The identity link

Number of hyperparmeters is 0.

Model 'inverse'.

Properties:

doc =

The inverse link

Number of hyperparmeters is 0.

Model 'log'.

Properties:

doc =

The log-link

Number of hyperparmeters is 0.

Model 'loga'.

Properties:

doc =

The loga-link

Number of hyperparmeters is 0.

Model 'neglog'.

Properties:

doc =

The negative log-link

Number of hyperparmeters is 0.

Model 'logit'.

Properties:

doc =

The logit-link

Number of hyperparmeters is 0.

Model 'probit'.

Properties:

doc =

The probit-link

Number of hyperparmeters is 0.

Model 'cauchit'.

Properties:

doc =

The cauchit-link

Number of hyperparmeters is 0.

Model 'tan'.

Properties:

doc =

The tan-link

pdf =

circular

Number of hyperparmeters is 0.

Model 'tanpi'.

Properties:

doc =

The tanpi-link

pdf =

circular

Number of hyperparmeters is 0.

Model 'quantile'.

Properties:

doc =

The quantile-link

Number of hyperparmeters is 0.

Model 'pquantile'.

Properties:

doc =

The population quantile-link

Number of hyperparmeters is 0.

Model 'sslogit'.

Properties:

doc =

Logit link with sensitivity and specificity

status =

disabled

pdf =

NA

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

48001

name =

sensitivity

short.name =

sens

prior =

logitbeta

param =

10 5

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

48002

name =

specificity

short.name =

spec

prior =

logitbeta

param =

10 5

initial =

1

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'logoffset'.

Properties:

doc =

Log-link with an offset

pdf =

logoffset

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

49001

name =

beta

short.name =

b

prior =

normal

param =

0 100

initial =

0

fixed =

TRUE

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'logitoffset'.

Properties:

doc =

Logit-link with an offset

pdf =

logitoffset

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

49011

name =

prob

short.name =

p

prior =

normal

param =

-1 100

initial =

-1

fixed =

FALSE

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'robit'.

Properties:

doc =

Robit link

pdf =

robit

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

49021

name =

log degrees of freedom

short.name =

dof

initial =

1.6094379124341

fixed =

TRUE

prior =

pc.dof

param =

50 0.5

to.theta =

function(x) log(x - 2)

from.theta =

function(x) 2 + exp(x)

Model 'sn'.

Properties:

doc =

Skew-normal link

pdf =

linksn

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

49031

name =

skewness

short.name =

skew

initial =

0.00123456789

fixed =

FALSE

prior =

pc.sn

param =

10

to.theta =

function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))

from.theta =

function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)

Hyperparameter 'theta2'

hyperid =

49032

name =

intercept

short.name =

p0

initial =

0

fixed =

FALSE

prior =

linksnintercept

param =

0 0

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'gevit'.

Properties:

doc =

GEVIT link

pdf =

gevit

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

49033

name =

gev tail

short.name =

tail

initial =

0.1

fixed =

FALSE

prior =

pc.egptail

param =

5 -0.5 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Hyperparameter 'theta2'

hyperid =

49034

name =

gev p0

short.name =

p0

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) 1 / (1 + exp(-x))

Model 'cgevit'.

Properties:

doc =

Complement GEVIT link

pdf =

gevit

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

49035

name =

gev tail

short.name =

tail

initial =

-3

fixed =

FALSE

prior =

pc.gevtail

param =

7 0 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Hyperparameter 'theta2'

hyperid =

49036

name =

gev p0

short.name =

p0

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) 1 / (1 + exp(-x))

Model 'powerlogit'.

Properties:

doc =

Power logit link

pdf =

powerlogit

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

49131

name =

power

short.name =

power

initial =

0.00123456789

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

49132

name =

intercept

short.name =

p0

initial =

0

fixed =

FALSE

prior =

logitbeta

param =

1 1

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'test1'.

Properties:

doc =

A test1-link function (experimental)

pdf =

NA

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

50001

name =

beta

short.name =

b

prior =

normal

param =

0 100

initial =

0

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

Model 'special1'.

Properties:

doc =

A special1-link function (experimental)

pdf =

NA

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

51001

name =

log precision

short.name =

prec

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

51002

name =

beta1

short.name =

beta1

initial =

0

fixed =

FALSE

prior =

mvnorm

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

51003

name =

beta2

short.name =

beta2

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

51004

name =

beta3

short.name =

beta3

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

51005

name =

beta4

short.name =

beta4

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

51006

name =

beta5

short.name =

beta5

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

51007

name =

beta6

short.name =

beta6

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

51008

name =

beta7

short.name =

beta7

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

51009

name =

beta8

short.name =

beta8

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

51010

name =

beta9

short.name =

beta9

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

51011

name =

beta10

short.name =

beta10

initial =

0

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) x

from.theta =

function(x) x

Model 'special2'.

Properties:

doc =

A special2-link function (experimental)

pdf =

NA

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

52001

name =

beta

short.name =

b

prior =

normal

param =

0 10

initial =

0

fixed =

FALSE

to.theta =

function(x) x

from.theta =

function(x) x

'predictor'

Valid models in this section are:

Model 'predictor'.

Properties:

doc =

(do not use)

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

53001

name =

log precision

short.name =

prec

initial =

13.8155105579643

fixed =

TRUE

prior =

loggamma

param =

1 1e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'hazard'

Valid models in this section are:

Model 'rw1'.

Properties:

doc =

A random walk of order 1 for the log-hazard

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

54001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'rw2'.

Properties:

doc =

A random walk of order 2 for the log-hazard

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

55001

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iid'.

Properties:

doc =

An iid model for the log-hazard

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

55501

name =

log precision

short.name =

prec

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'likelihood'

Valid models in this section are:

Model 'fl'.

Properties:

doc =

The fl likelihood

survival =

FALSE

discrete =

TRUE

link =

default identity

pdf =

fl

Number of hyperparmeters is 0.

Model 'poisson'.

Properties:

doc =

The Poisson likelihood

survival =

FALSE

discrete =

TRUE

link =

default log logoffset quantile test1 special1 special2

pdf =

poisson

Number of hyperparmeters is 0.

Model 'npoisson'.

Properties:

doc =

The Normal approximation to the Poisson likelihood

survival =

FALSE

discrete =

TRUE

link =

default log logoffset

pdf =

poisson

Number of hyperparmeters is 0.

Model 'nzpoisson'.

Properties:

doc =

The nzPoisson likelihood

survival =

FALSE

discrete =

TRUE

link =

default log logoffset

pdf =

nzpoisson

Number of hyperparmeters is 0.

Model 'xpoisson'.

Properties:

doc =

The Poisson likelihood (expert version)

survival =

FALSE

discrete =

TRUE

link =

default log logoffset quantile test1 special1 special2

pdf =

poisson

Number of hyperparmeters is 0.

Model 'cenpoisson'.

Properties:

doc =

Then censored Poisson likelihood

survival =

FALSE

discrete =

TRUE

link =

default log logoffset test1 special1 special2

pdf =

cenpoisson

Number of hyperparmeters is 0.

Model 'cenpoisson2'.

Properties:

doc =

Then censored Poisson likelihood (version 2)

survival =

FALSE

discrete =

TRUE

link =

default log logoffset test1 special1 special2

pdf =

cenpoisson2

Number of hyperparmeters is 0.

Model 'gpoisson'.

Properties:

doc =

The generalized Poisson likelihood

survival =

FALSE

discrete =

TRUE

link =

default log logoffset

pdf =

gpoisson

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

56001

name =

overdispersion

short.name =

phi

output.name =

Overdispersion for gpoisson

output.name.intern =

Log overdispersion for gpoisson

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

56002

name =

p

short.name =

p

output.name =

Parameter p for gpoisson

output.name.intern =

Parameter p_intern for gpoisson

initial =

1

fixed =

TRUE

prior =

normal

param =

1 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'poisson.special1'.

Properties:

doc =

The Poisson.special1 likelihood

survival =

FALSE

discrete =

TRUE

link =

default log

pdf =

poisson-special

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

56100

name =

logit probability

short.name =

prob

output.name =

one-probability parameter for poisson.special1

output.name.intern =

intern one-probability parameter for poisson.special1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model '0poisson'.

Properties:

doc =

New 0-inflated Poisson

survival =

FALSE

discrete =

TRUE

link =

default log quantile

link.simple =

default logit cauchit probit cloglog ccloglog

pdf =

0inflated

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56201

name =

beta1

short.name =

beta1

output.name =

beta1 for 0poisson observations

output.name.intern =

beta1 for 0poisson observations

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56202

name =

beta2

short.name =

beta2

output.name =

beta2 for 0poisson observations

output.name.intern =

beta2 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56203

name =

beta3

short.name =

beta3

output.name =

beta3 for 0poisson observations

output.name.intern =

beta3 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56204

name =

beta4

short.name =

beta4

output.name =

beta4 for 0poisson observations

output.name.intern =

beta4 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56205

name =

beta5

short.name =

beta5

output.name =

beta5 for 0poisson observations

output.name.intern =

beta5 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56206

name =

beta6

short.name =

beta6

output.name =

beta6 for 0poisson observations

output.name.intern =

beta6 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56207

name =

beta7

short.name =

beta7

output.name =

beta7 for 0poisson observations

output.name.intern =

beta7 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56208

name =

beta8

short.name =

beta8

output.name =

beta8 for 0poisson observations

output.name.intern =

beta8 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56209

name =

beta9

short.name =

beta9

output.name =

beta9 for 0poisson observations

output.name.intern =

beta9 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56210

name =

beta10

short.name =

beta10

output.name =

beta10 for 0poisson observations

output.name.intern =

beta10 for 0poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model '0poissonS'.

Properties:

doc =

New 0-inflated Poisson Swap

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit

link.simple =

default log

pdf =

0inflated

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56301

name =

beta1

short.name =

beta1

output.name =

beta1 for 0poissonS observations

output.name.intern =

beta1 for 0poissonS observations

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56302

name =

beta2

short.name =

beta2

output.name =

beta2 for 0poissonS observations

output.name.intern =

beta2 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56303

name =

beta3

short.name =

beta3

output.name =

beta3 for 0poissonS observations

output.name.intern =

beta3 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56304

name =

beta4

short.name =

beta4

output.name =

beta4 for 0poissonS observations

output.name.intern =

beta4 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56305

name =

beta5

short.name =

beta5

output.name =

beta5 for 0poissonS observations

output.name.intern =

beta5 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56306

name =

beta6

short.name =

beta6

output.name =

beta6 for 0poissonS observations

output.name.intern =

beta6 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56307

name =

beta7

short.name =

beta7

output.name =

beta7 for 0poissonS observations

output.name.intern =

beta7 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56308

name =

beta8

short.name =

beta8

output.name =

beta8 for 0poissonS observations

output.name.intern =

beta8 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56309

name =

beta9

short.name =

beta9

output.name =

beta9 for 0poissonS observations

output.name.intern =

beta9 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56310

name =

beta10

short.name =

beta10

output.name =

beta10 for 0poissonS observations

output.name.intern =

beta10 for 0poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model '1poisson'.

Properties:

doc =

New 1-inflated Poisson

survival =

FALSE

discrete =

TRUE

link =

default log quantile

link.simple =

default logit cauchit probit cloglog ccloglog

pdf =

0inflated

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56401

name =

beta1

short.name =

beta1

output.name =

beta1 for 1poisson observations

output.name.intern =

beta1 for 1poisson observations

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56402

name =

beta2

short.name =

beta2

output.name =

beta2 for 1poisson observations

output.name.intern =

beta2 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56403

name =

beta3

short.name =

beta3

output.name =

beta3 for 1poisson observations

output.name.intern =

beta3 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56404

name =

beta4

short.name =

beta4

output.name =

beta4 for 1poisson observations

output.name.intern =

beta4 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56405

name =

beta5

short.name =

beta5

output.name =

beta5 for 1poisson observations

output.name.intern =

beta5 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56406

name =

beta6

short.name =

beta6

output.name =

beta6 for 1poisson observations

output.name.intern =

beta6 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56407

name =

beta7

short.name =

beta7

output.name =

beta7 for 1poisson observations

output.name.intern =

beta7 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56408

name =

beta8

short.name =

beta8

output.name =

beta8 for 1poisson observations

output.name.intern =

beta8 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56409

name =

beta9

short.name =

beta9

output.name =

beta9 for 1poisson observations

output.name.intern =

beta9 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56410

name =

beta10

short.name =

beta10

output.name =

beta10 for 1poisson observations

output.name.intern =

beta10 for 1poisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model '1poissonS'.

Properties:

doc =

New 1-inflated Poisson Swap

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit

link.simple =

default log

pdf =

0inflated

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56501

name =

beta1

short.name =

beta1

output.name =

beta1 for 1poissonS observations

output.name.intern =

beta1 for 1poissonS observations

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56502

name =

beta2

short.name =

beta2

output.name =

beta2 for 1poissonS observations

output.name.intern =

beta2 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56503

name =

beta3

short.name =

beta3

output.name =

beta3 for 1poissonS observations

output.name.intern =

beta3 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56504

name =

beta4

short.name =

beta4

output.name =

beta4 for 1poissonS observations

output.name.intern =

beta4 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56505

name =

beta5

short.name =

beta5

output.name =

beta5 for 1poissonS observations

output.name.intern =

beta5 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56506

name =

beta6

short.name =

beta6

output.name =

beta6 for 1poissonS observations

output.name.intern =

beta6 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56507

name =

beta7

short.name =

beta7

output.name =

beta7 for 1poissonS observations

output.name.intern =

beta7 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56508

name =

beta8

short.name =

beta8

output.name =

beta8 for 1poissonS observations

output.name.intern =

beta8 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56509

name =

beta9

short.name =

beta9

output.name =

beta9 for 1poissonS observations

output.name.intern =

beta9 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56510

name =

beta10

short.name =

beta10

output.name =

beta10 for 1poissonS observations

output.name.intern =

beta10 for 1poissonS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'bell'.

Properties:

doc =

The Bell likelihood

survival =

FALSE

discrete =

TRUE

link =

default log

pdf =

bell

Number of hyperparmeters is 0.

Model '0binomial'.

Properties:

doc =

New 0-inflated Binomial

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog log

link.simple =

default logit cauchit probit cloglog ccloglog

pdf =

0inflated

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56401

name =

beta1

short.name =

beta1

output.name =

beta1 for 0binomial observations

output.name.intern =

beta1 for 0binomial observations

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56402

name =

beta2

short.name =

beta2

output.name =

beta2 for 0binomial observations

output.name.intern =

beta2 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56403

name =

beta3

short.name =

beta3

output.name =

beta3 for 0binomial observations

output.name.intern =

beta3 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56404

name =

beta4

short.name =

beta4

output.name =

beta4 for 0binomial observations

output.name.intern =

beta4 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56405

name =

beta5

short.name =

beta5

output.name =

beta5 for 0binomial observations

output.name.intern =

beta5 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56406

name =

beta6

short.name =

beta6

output.name =

beta6 for 0binomial observations

output.name.intern =

beta6 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56407

name =

beta7

short.name =

beta7

output.name =

beta7 for 0binomial observations

output.name.intern =

beta7 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56408

name =

beta8

short.name =

beta8

output.name =

beta8 for 0binomial observations

output.name.intern =

beta8 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56409

name =

beta9

short.name =

beta9

output.name =

beta9 for 0binomial observations

output.name.intern =

beta9 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56410

name =

beta10

short.name =

beta10

output.name =

beta10 for 0binomial observations

output.name.intern =

beta10 for 0binomial observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model '0binomialS'.

Properties:

doc =

New 0-inflated Binomial Swap

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog log

link.simple =

default logit cauchit probit cloglog ccloglog

pdf =

0inflated

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56501

name =

beta1

short.name =

beta1

output.name =

beta1 for 0binomialS observations

output.name.intern =

beta1 for 0binomialS observations

initial =

-4

fixed =

FALSE

prior =

normal

param =

-4 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56502

name =

beta2

short.name =

beta2

output.name =

beta2 for 0binomialS observations

output.name.intern =

beta2 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56503

name =

beta3

short.name =

beta3

output.name =

beta3 for 0binomialS observations

output.name.intern =

beta3 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56504

name =

beta4

short.name =

beta4

output.name =

beta4 for 0binomialS observations

output.name.intern =

beta4 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56505

name =

beta5

short.name =

beta5

output.name =

beta5 for 0binomialS observations

output.name.intern =

beta5 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56506

name =

beta6

short.name =

beta6

output.name =

beta6 for 0binomialS observations

output.name.intern =

beta6 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56507

name =

beta7

short.name =

beta7

output.name =

beta7 for 0binomialS observations

output.name.intern =

beta7 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56508

name =

beta8

short.name =

beta8

output.name =

beta8 for 0binomialS observations

output.name.intern =

beta8 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56509

name =

beta9

short.name =

beta9

output.name =

beta9 for 0binomialS observations

output.name.intern =

beta9 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56510

name =

beta10

short.name =

beta10

output.name =

beta10 for 0binomialS observations

output.name.intern =

beta10 for 0binomialS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'binomialmix'.

Properties:

doc =

Binomial mixture

survival =

FALSE

discrete =

TRUE

link =

default logit probit

pdf =

binomialmix

Number of hyperparmeters is 51.

Hyperparameter 'theta1'

hyperid =

56551

name =

beta1

short.name =

beta1

output.name =

beta1 for binomialmix observations

output.name.intern =

beta1 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56552

name =

beta2

short.name =

beta2

output.name =

beta2 for binomialmix observations

output.name.intern =

beta2 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56553

name =

beta3

short.name =

beta3

output.name =

beta3 for binomialmix observations

output.name.intern =

beta3 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56554

name =

beta4

short.name =

beta4

output.name =

beta4 for binomialmix observations

output.name.intern =

beta4 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56555

name =

beta5

short.name =

beta5

output.name =

beta5 for binomialmix observations

output.name.intern =

beta5 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56556

name =

beta6

short.name =

beta6

output.name =

beta6 for binomialmix observations

output.name.intern =

beta6 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56557

name =

beta7

short.name =

beta7

output.name =

beta7 for binomialmix observations

output.name.intern =

beta7 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56558

name =

beta8

short.name =

beta8

output.name =

beta8 for binomialmix observations

output.name.intern =

beta8 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56559

name =

beta9

short.name =

beta9

output.name =

beta9 for binomialmix observations

output.name.intern =

beta9 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56560

name =

beta10

short.name =

beta10

output.name =

beta10 for binomialmix observations

output.name.intern =

beta10 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

56561

name =

beta11

short.name =

beta11

output.name =

beta11 for binomialmix observations

output.name.intern =

beta11 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

56562

name =

beta12

short.name =

beta12

output.name =

beta12 for binomialmix observations

output.name.intern =

beta12 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

56563

name =

beta13

short.name =

beta13

output.name =

beta13 for binomialmix observations

output.name.intern =

beta13 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

56564

name =

beta14

short.name =

beta14

output.name =

beta14 for binomialmix observations

output.name.intern =

beta14 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

56565

name =

beta15

short.name =

beta15

output.name =

beta15 for binomialmix observations

output.name.intern =

beta15 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

56566

name =

beta16

short.name =

beta16

output.name =

beta16 for binomialmix observations

output.name.intern =

beta16 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

56567

name =

beta17

short.name =

beta17

output.name =

beta17 for binomialmix observations

output.name.intern =

beta17 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

56568

name =

beta18

short.name =

beta18

output.name =

beta18 for binomialmix observations

output.name.intern =

beta18 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

56569

name =

beta19

short.name =

beta19

output.name =

beta19 for binomialmix observations

output.name.intern =

beta19 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

56570

name =

beta20

short.name =

beta20

output.name =

beta20 for binomialmix observations

output.name.intern =

beta20 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

56571

name =

beta21

short.name =

beta21

output.name =

beta21 for binomialmix observations

output.name.intern =

beta21 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

56572

name =

beta22

short.name =

beta22

output.name =

beta22 for binomialmix observations

output.name.intern =

beta22 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

56573

name =

beta23

short.name =

beta23

output.name =

beta23 for binomialmix observations

output.name.intern =

beta23 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

56574

name =

beta24

short.name =

beta24

output.name =

beta24 for binomialmix observations

output.name.intern =

beta24 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

56575

name =

beta25

short.name =

beta25

output.name =

beta25 for binomialmix observations

output.name.intern =

beta25 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

56576

name =

beta26

short.name =

beta26

output.name =

beta26 for binomialmix observations

output.name.intern =

beta26 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

56577

name =

beta27

short.name =

beta27

output.name =

beta27 for binomialmix observations

output.name.intern =

beta27 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

56578

name =

beta28

short.name =

beta28

output.name =

beta28 for binomialmix observations

output.name.intern =

beta28 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

56579

name =

beta29

short.name =

beta29

output.name =

beta29 for binomialmix observations

output.name.intern =

beta29 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

56580

name =

beta30

short.name =

beta30

output.name =

beta30 for binomialmix observations

output.name.intern =

beta30 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

56581

name =

beta31

short.name =

beta31

output.name =

beta31 for binomialmix observations

output.name.intern =

beta31 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

56582

name =

beta32

short.name =

beta32

output.name =

beta32 for binomialmix observations

output.name.intern =

beta32 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

56583

name =

beta33

short.name =

beta33

output.name =

beta33 for binomialmix observations

output.name.intern =

beta33 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

56584

name =

beta34

short.name =

beta34

output.name =

beta34 for binomialmix observations

output.name.intern =

beta34 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

56585

name =

beta35

short.name =

beta35

output.name =

beta35 for binomialmix observations

output.name.intern =

beta35 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

56586

name =

beta36

short.name =

beta36

output.name =

beta36 for binomialmix observations

output.name.intern =

beta36 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

56587

name =

beta37

short.name =

beta37

output.name =

beta37 for binomialmix observations

output.name.intern =

beta37 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

56588

name =

beta38

short.name =

beta38

output.name =

beta38 for binomialmix observations

output.name.intern =

beta38 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

56589

name =

beta39

short.name =

beta39

output.name =

beta39 for binomialmix observations

output.name.intern =

beta39 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

56590

name =

beta40

short.name =

beta40

output.name =

beta40 for binomialmix observations

output.name.intern =

beta40 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

56591

name =

beta41

short.name =

beta41

output.name =

beta41 for binomialmix observations

output.name.intern =

beta41 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

56592

name =

beta42

short.name =

beta42

output.name =

beta42 for binomialmix observations

output.name.intern =

beta42 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

56593

name =

beta43

short.name =

beta43

output.name =

beta43 for binomialmix observations

output.name.intern =

beta43 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

56594

name =

beta44

short.name =

beta44

output.name =

beta44 for binomialmix observations

output.name.intern =

beta44 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

56595

name =

beta45

short.name =

beta45

output.name =

beta45 for binomialmix observations

output.name.intern =

beta45 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

56596

name =

beta46

short.name =

beta46

output.name =

beta46 for binomialmix observations

output.name.intern =

beta46 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

56597

name =

beta47

short.name =

beta47

output.name =

beta47 for binomialmix observations

output.name.intern =

beta47 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

56598

name =

beta48

short.name =

beta48

output.name =

beta48 for binomialmix observations

output.name.intern =

beta48 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

56599

name =

beta49

short.name =

beta49

output.name =

beta49 for binomialmix observations

output.name.intern =

beta49 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

56600

name =

beta50

short.name =

beta50

output.name =

beta50 for binomialmix observations

output.name.intern =

beta50 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

56601

name =

beta51

short.name =

beta51

output.name =

beta51 for binomialmix observations

output.name.intern =

beta51 for binomialmix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'binomial'.

Properties:

doc =

The Binomial likelihood

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit gevit cgevit

pdf =

binomial

Number of hyperparmeters is 0.

Model 'xbinomial'.

Properties:

doc =

The Binomial likelihood (experimental version)

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog log sslogit logitoffset quantile pquantile robit sn powerlogit gevit cgevit

pdf =

binomial

Number of hyperparmeters is 0.

Model 'occupancy'.

Properties:

doc =

Occupancy likelihood

survival =

FALSE

discrete =

TRUE

link =

default logit cloglog

link.simple =

default logit cloglog

pdf =

occupancy

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

56601

name =

beta1

short.name =

beta1

output.name =

beta1 for occupancy observations

output.name.intern =

beta1 for occupancy observations

initial =

-2

fixed =

FALSE

prior =

normal

param =

-2 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

56602

name =

beta2

short.name =

beta2

output.name =

beta2 for occupancy observations

output.name.intern =

beta2 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

56603

name =

beta3

short.name =

beta3

output.name =

beta3 for occupancy observations

output.name.intern =

beta3 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

56604

name =

beta4

short.name =

beta4

output.name =

beta4 for occupancy observations

output.name.intern =

beta4 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

56605

name =

beta5

short.name =

beta5

output.name =

beta5 for occupancy observations

output.name.intern =

beta5 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

56606

name =

beta6

short.name =

beta6

output.name =

beta6 for occupancy observations

output.name.intern =

beta6 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

56607

name =

beta7

short.name =

beta7

output.name =

beta7 for occupancy observations

output.name.intern =

beta7 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

56608

name =

beta8

short.name =

beta8

output.name =

beta8 for occupancy observations

output.name.intern =

beta8 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

56609

name =

beta9

short.name =

beta9

output.name =

beta9 for occupancy observations

output.name.intern =

beta9 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

56610

name =

beta10

short.name =

beta10

output.name =

beta10 for occupancy observations

output.name.intern =

beta10 for occupancy observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'pom'.

Properties:

doc =

Likelihood for the proportional odds model

survival =

FALSE

discrete =

TRUE

link =

default identity

pdf =

pom

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

57101

name =

theta1

short.name =

theta1

output.name =

theta1 for POM

output.name.intern =

theta1 for POM

initial =

NA

fixed =

FALSE

prior =

dirichlet

param =

3

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

57102

name =

theta2

short.name =

theta2

output.name =

theta2 for POM

output.name.intern =

theta2 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

57103

name =

theta3

short.name =

theta3

output.name =

theta3 for POM

output.name.intern =

theta3 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

57104

name =

theta4

short.name =

theta4

output.name =

theta4 for POM

output.name.intern =

theta4 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

57105

name =

theta5

short.name =

theta5

output.name =

theta5 for POM

output.name.intern =

theta5 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

57106

name =

theta6

short.name =

theta6

output.name =

theta6 for POM

output.name.intern =

theta6 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

57107

name =

theta7

short.name =

theta7

output.name =

theta7 for POM

output.name.intern =

theta7 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

57108

name =

theta8

short.name =

theta8

output.name =

theta8 for POM

output.name.intern =

theta8 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

57109

name =

theta9

short.name =

theta9

output.name =

theta9 for POM

output.name.intern =

theta9 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

57110

name =

theta10

short.name =

theta10

output.name =

theta10 for POM

output.name.intern =

theta10 for POM

initial =

NA

fixed =

FALSE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'bgev'.

Properties:

doc =

The blended Generalized Extreme Value likelihood

survival =

FALSE

discrete =

FALSE

link =

default identity log

pdf =

bgev

Number of hyperparmeters is 12.

Hyperparameter 'theta1'

hyperid =

57201

name =

spread

short.name =

sd

output.name =

spread for BGEV observations

output.name.intern =

log spread for BGEV observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 3

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

57202

name =

tail

short.name =

xi

output.name =

tail for BGEV observations

output.name.intern =

intern tail for BGEV observations

initial =

-4

fixed =

FALSE

prior =

pc.gevtail

param =

7 0 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Hyperparameter 'theta3'

hyperid =

57203

name =

beta1

short.name =

beta1

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

57204

name =

beta2

short.name =

beta2

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

57205

name =

beta3

short.name =

beta3

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

57206

name =

beta4

short.name =

beta4

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

57207

name =

beta5

short.name =

beta5

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

57208

name =

beta6

short.name =

beta6

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

57209

name =

beta7

short.name =

beta7

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

57210

name =

beta8

short.name =

beta8

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

57211

name =

beta9

short.name =

beta9

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

57212

name =

beta10

short.name =

beta

output.name =

MUST BE FIXED

output.name.intern =

MUST BE FIXED

initial =

NA

fixed =

FALSE

prior =

normal

param =

0 300

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gamma'.

Properties:

doc =

The Gamma likelihood

survival =

FALSE

discrete =

FALSE

link =

default log quantile

pdf =

gamma

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

58001

name =

precision parameter

short.name =

prec

output.name =

Precision-parameter for the Gamma observations

output.name.intern =

Intern precision-parameter for the Gamma observations

initial =

4.60517018598809

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'mgamma'.

Properties:

doc =

The modal Gamma likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

mgamma

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

58002

name =

precision parameter

short.name =

prec

output.name =

Precision-parameter for the modal Gamma observations

output.name.intern =

Intern precision-parameter for the modal Gamma observations

initial =

4.60517018598809

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gammasv'.

Properties:

doc =

The Gamma likelihood with constant rate

status =

experimental

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

gammasv

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

58003

name =

precision parameter

short.name =

prec

output.name =

Precision-parameter for the Gammasv observations

output.name.intern =

Intern precision-parameter for the Gammasv observations

initial =

4.60517018598809

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gammasurv'.

Properties:

doc =

The Gamma likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default log neglog quantile

pdf =

gammasurv

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

58101

name =

precision parameter

short.name =

prec

output.name =

Precision-parameter for the Gamma surv observations

output.name.intern =

Intern precision-parameter for the Gamma surv observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

58102

name =

beta1

short.name =

beta1

output.name =

beta1 for Gamma-Cure

output.name.intern =

beta1 for Gamma-Cure

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

58103

name =

beta2

short.name =

beta2

output.name =

beta2 for Gamma-Cure

output.name.intern =

beta2 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

58104

name =

beta3

short.name =

beta3

output.name =

beta3 for Gamma-Cure

output.name.intern =

beta3 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

58105

name =

beta4

short.name =

beta4

output.name =

beta4 for Ga mma-Cure

output.name.intern =

beta4 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

58106

name =

beta5

short.name =

beta5

output.name =

beta5 for Gamma-Cure

output.name.intern =

beta5 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

58107

name =

beta6

short.name =

beta6

output.name =

beta6 for Gamma-Cure

output.name.intern =

beta6 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

58108

name =

beta7

short.name =

beta7

output.name =

beta7 for Gamma-Cure

output.name.intern =

beta7 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

58109

name =

beta8

short.name =

beta8

output.name =

beta8 for Gamma-Cure

output.name.intern =

beta8 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

58110

name =

beta9

short.name =

beta9

output.name =

beta9 for Gamma-Cure

output.name.intern =

beta9 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

58111

name =

beta10

short.name =

beta10

output.name =

beta10 for Gamma-Cure

output.name.intern =

beta10 for Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'mgammasurv'.

Properties:

doc =

The modal Gamma likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default log neglog

pdf =

agamma

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

58121

name =

precision parameter

short.name =

prec

output.name =

Precision-parameter for the modal Gamma surv observations

output.name.intern =

Intern precision-parameter for the modal Gamma surv observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

58122

name =

beta1

short.name =

beta1

output.name =

beta1 for modal Gamma-Cure

output.name.intern =

beta1 for modal Gamma-Cure

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

58123

name =

beta2

short.name =

beta2

output.name =

beta2 for modal Gamma-Cure

output.name.intern =

beta2 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

58124

name =

beta3

short.name =

beta3

output.name =

beta3 for modal Gamma-Cure

output.name.intern =

beta3 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

58125

name =

beta4

short.name =

beta4

output.name =

beta4 for Ga mma-Cure

output.name.intern =

beta4 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

58126

name =

beta5

short.name =

beta5

output.name =

beta5 for modal Gamma-Cure

output.name.intern =

beta5 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

58127

name =

beta6

short.name =

beta6

output.name =

beta6 for modal Gamma-Cure

output.name.intern =

beta6 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

58128

name =

beta7

short.name =

beta7

output.name =

beta7 for modal Gamma-Cure

output.name.intern =

beta7 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

58129

name =

beta8

short.name =

beta8

output.name =

beta8 for modal Gamma-Cure

output.name.intern =

beta8 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

58130

name =

beta9

short.name =

beta9

output.name =

beta9 for modal Gamma-Cure

output.name.intern =

beta9 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

58131

name =

beta10

short.name =

beta10

output.name =

beta10 for modal Gamma-Cure

output.name.intern =

beta10 for modal Gamma-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gammajw'.

Properties:

doc =

A special case of the Gamma likelihood

survival =

FALSE

discrete =

FALSE

link =

default log neglog

pdf =

gammajw

Number of hyperparmeters is 0.

Model 'gammajwsurv'.

Properties:

doc =

A special case of the Gamma likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default log

pdf =

gammajw

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

58200

name =

beta1

short.name =

beta1

output.name =

beta1 for GammaJW-Cure

output.name.intern =

beta1 for GammaJW-Cure

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

58201

name =

beta2

short.name =

beta2

output.name =

beta1 for GammaJW-Cure

output.name.intern =

beta1 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

58202

name =

beta3

short.name =

beta3

output.name =

beta3 for GammaJW-Cure

output.name.intern =

beta3 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

58203

name =

beta4

short.name =

beta4

output.name =

beta4 for GammaJW-Cure

output.name.intern =

beta4 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

58204

name =

beta5

short.name =

beta5

output.name =

beta5 for GammaJW-Cure

output.name.intern =

beta5 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

58205

name =

beta6

short.name =

beta6

output.name =

beta6 for GammaJW-Cure

output.name.intern =

beta6 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

58206

name =

beta7

short.name =

beta7

output.name =

beta7 for GammaJW-Cure

output.name.intern =

beta7 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

58207

name =

beta8

short.name =

beta8

output.name =

beta8 for GammaJW-Cure

output.name.intern =

beta8 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

58208

name =

beta9

short.name =

beta9

output.name =

beta9 for GammaJW-Cure

output.name.intern =

beta9 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

58209

name =

beta10

short.name =

beta10

output.name =

beta10 for GammaJW-Cure

output.name.intern =

beta10 for GammaJW-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gammacount'.

Properties:

doc =

A Gamma generalisation of the Poisson likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

gammacount

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

59001

name =

log alpha

short.name =

alpha

output.name =

Log-alpha parameter for Gammacount observations

output.name.intern =

Alpha parameter for Gammacount observations

initial =

0

fixed =

FALSE

prior =

pc.gammacount

param =

3

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'qkumar'.

Properties:

doc =

A quantile version of the Kumar likelihood

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit

pdf =

qkumar

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

60001

name =

precision parameter

short.name =

prec

output.name =

precision for qkumar observations

output.name.intern =

log precision for qkumar observations

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 0.1

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Model 'qloglogistic'.

Properties:

doc =

A quantile loglogistic likelihood

survival =

FALSE

discrete =

FALSE

link =

default log neglog

pdf =

qloglogistic

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

60011

name =

log alpha

short.name =

alpha

output.name =

alpha for qloglogistic observations

output.name.intern =

log alpha for qloglogistic observations

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'qloglogisticsurv'.

Properties:

doc =

A quantile loglogistic likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default log neglog

pdf =

qloglogistic

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

60021

name =

log alpha

short.name =

alpha

output.name =

alpha for qloglogisticsurv observations

output.name.intern =

log alpha for qloglogisticsurv observations

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

60022

name =

beta1

short.name =

beta1

output.name =

beta1 for qlogLogistic-Cure

output.name.intern =

beta1 for logLogistic-Cure

initial =

-5

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

60023

name =

beta2

short.name =

beta2

output.name =

beta2 for qlogLogistic-Cure

output.name.intern =

beta2 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

60024

name =

beta3

short.name =

beta3

output.name =

beta3 for qlogLogistic-Cure

output.name.intern =

beta3 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

60025

name =

beta4

short.name =

beta4

output.name =

beta4 for qlogLogistic-Cure

output.name.intern =

beta4 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

60026

name =

beta5

short.name =

beta5

output.name =

beta5 for qlogLogistic-Cure

output.name.intern =

beta5 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

60027

name =

beta6

short.name =

beta6

output.name =

beta6 for qlogLogistic-Cure

output.name.intern =

beta6 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

60028

name =

beta7

short.name =

beta7

output.name =

beta7 for qlogLogistic-Cure

output.name.intern =

beta7 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

60029

name =

beta8

short.name =

beta8

output.name =

beta8 for qlogLogistic-Cure

output.name.intern =

beta8 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

60030

name =

beta9

short.name =

beta9

output.name =

beta9 for qlogLogistic-Cure

output.name.intern =

beta9 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

60031

name =

beta10

short.name =

beta10

output.name =

beta10 for qlogLogistic-Cure

output.name.intern =

beta10 for qlogLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'beta'.

Properties:

doc =

The Beta likelihood

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog

pdf =

beta

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

61001

name =

precision parameter

short.name =

phi

output.name =

precision parameter for the beta observations

output.name.intern =

intern precision-parameter for the beta observations

initial =

2.30258509299405

fixed =

FALSE

prior =

loggamma

param =

1 0.1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'obeta'.

Properties:

doc =

The ordered Beta likelihood

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog

pdf =

obeta

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

61101

name =

precision parameter

short.name =

phi

output.name =

precision-parameter for the obeta observations

output.name.intern =

intern precision-parameter for the obeta observations

initial =

2.30258509299405

fixed =

FALSE

prior =

loggamma

param =

1 0.1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

61102

name =

offset location

short.name =

loc

output.name =

offset location-parameter for the obeta observations

output.name =

offset location-parameter for the obeta observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

61103

name =

offset width

short.name =

width

output.name =

offset width-parameter for the obeta observations

output.name =

offset width-parameter for the obeta observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'betabinomial'.

Properties:

doc =

The Beta-Binomial likelihood

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

betabinomial

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

62001

name =

overdispersion

short.name =

rho

output.name =

overdispersion for the betabinomial observations

output.name.intern =

intern overdispersion for the betabinomial observations

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'betabinomialna'.

Properties:

doc =

The Beta-Binomial Normal approximation likelihood

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

betabinomialna

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

62101

name =

overdispersion

short.name =

rho

output.name =

overdispersion for the betabinomialna observations

output.name.intern =

intern overdispersion for the betabinomialna observations

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'cbinomial'.

Properties:

doc =

The clustered Binomial likelihood

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

cbinomial

Number of hyperparmeters is 0.

Model 'nbinomial'.

Properties:

doc =

The negBinomial likelihood

survival =

FALSE

discrete =

TRUE

link =

default log logoffset quantile

pdf =

nbinomial

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

63001

name =

size

short.name =

size

output.name =

size for the nbinomial observations (1/overdispersion)

output.name.intern =

log size for the nbinomial observations (1/overdispersion)

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'nbinomial2'.

Properties:

doc =

The negBinomial2 likelihood

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog

pdf =

nbinomial

Number of hyperparmeters is 0.

Model 'cennbinomial2'.

Properties:

doc =

The CenNegBinomial2 likelihood (similar to cenpoisson2)

survival =

FALSE

discrete =

TRUE

link =

default log logoffset quantile

pdf =

cennbinomial2

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

63101

name =

size

short.name =

size

output.name =

size for the cennbinomial2 observations (1/overdispersion)

output.name.intern =

log size for the cennbinomial2 observations (1/overdispersion)

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'simplex'.

Properties:

doc =

The simplex likelihood

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog

pdf =

simplex

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

64001

name =

log precision

short.name =

prec

output.name =

Precision for the Simplex observations

output.name.intern =

Log precision for the Simplex observations

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gaussian'.

Properties:

doc =

The Gaussian likelihoood

survival =

FALSE

discrete =

FALSE

link =

default identity logit loga cauchit log logoffset

pdf =

gaussian

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

65001

name =

log precision

short.name =

prec

output.name =

Precision for the Gaussian observations

output.name.intern =

Log precision for the Gaussian observations

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

65002

name =

log precision offset

short.name =

precoffset

output.name =

NOT IN USE

output.name.intern =

NOT IN USE

initial =

72.0873067782343

fixed =

TRUE

prior =

none

param =

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'stdgaussian'.

Properties:

doc =

The stdGaussian likelihoood

survival =

FALSE

discrete =

FALSE

link =

default identity logit loga cauchit log logoffset

pdf =

gaussian

Number of hyperparmeters is 0.

Model 'gaussianjw'.

Properties:

doc =

The GaussianJW likelihoood

survival =

FALSE

discrete =

FALSE

link =

default logit probit

pdf =

gaussianjw

Number of hyperparmeters is 3.

Hyperparameter 'theta1'

hyperid =

65101

name =

beta1

short.name =

beta1

output.name =

beta1 for GaussianJW observations

output.name.intern =

beta1 for GaussianJW observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

65102

name =

beta2

short.name =

beta2

output.name =

beta2 for GaussianJW observations

output.name.intern =

beta2 for GaussianJW observations

initial =

1

fixed =

FALSE

prior =

normal

param =

1 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

65103

name =

beta3

short.name =

beta3

output.name =

beta3 for GaussianJW observations

output.name.intern =

beta3 for GaussianJW observations

initial =

-1

fixed =

FALSE

prior =

normal

param =

-1 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'agaussian'.

Properties:

doc =

The aggregated Gaussian likelihoood

survival =

FALSE

discrete =

FALSE

link =

default identity logit loga cauchit log logoffset

pdf =

agaussian

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

66001

name =

log precision

short.name =

prec

output.name =

Precision for the AggGaussian observations

output.name.intern =

Log precision for the AggGaussian observations

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'ggaussian'.

Properties:

doc =

Generalized Gaussian

survival =

FALSE

discrete =

FALSE

link =

default identity

link.simple =

default log

pdf =

ggaussian

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

66501

name =

beta1

short.name =

beta1

output.name =

beta1 for ggaussian observations

output.name.intern =

beta1 for ggaussian observations

initial =

4

fixed =

FALSE

prior =

normal

param =

9.33 0.61

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

66502

name =

beta2

short.name =

beta2

output.name =

beta2 for ggaussian observations

output.name.intern =

beta2 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

66503

name =

beta3

short.name =

beta3

output.name =

beta3 for ggaussian observations

output.name.intern =

beta3 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

66504

name =

beta4

short.name =

beta4

output.name =

beta4 for ggaussian observations

output.name.intern =

beta4 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

66505

name =

beta5

short.name =

beta5

output.name =

beta5 for ggaussian observations

output.name.intern =

beta5 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

66506

name =

beta6

short.name =

beta6

output.name =

beta6 for ggaussian observations

output.name.intern =

beta6 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

66507

name =

beta7

short.name =

beta7

output.name =

beta7 for ggaussian observations

output.name.intern =

beta7 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

66508

name =

beta8

short.name =

beta8

output.name =

beta8 for ggaussian observations

output.name.intern =

beta8 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

66509

name =

beta9

short.name =

beta9

output.name =

beta9 for ggaussian observations

output.name.intern =

beta9 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

66510

name =

beta10

short.name =

beta10

output.name =

beta10 for ggaussian observations

output.name.intern =

beta10 for ggaussian observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'ggaussianS'.

Properties:

doc =

Generalized GaussianS

survival =

FALSE

discrete =

FALSE

link =

default log

link.simple =

default identity

pdf =

ggaussian

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

66601

name =

beta1

short.name =

beta1

output.name =

beta1 for ggaussianS observations

output.name.intern =

beta1 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

66602

name =

beta2

short.name =

beta2

output.name =

beta2 for ggaussianS observations

output.name.intern =

beta2 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

66603

name =

beta3

short.name =

beta3

output.name =

beta3 for ggaussianS observations

output.name.intern =

beta3 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

66604

name =

beta4

short.name =

beta4

output.name =

beta4 for ggaussianS observations

output.name.intern =

beta4 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

66605

name =

beta5

short.name =

beta5

output.name =

beta5 for ggaussianS observations

output.name.intern =

beta5 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

66606

name =

beta6

short.name =

beta6

output.name =

beta6 for ggaussianS observations

output.name.intern =

beta6 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

66607

name =

beta7

short.name =

beta7

output.name =

beta7 for ggaussianS observations

output.name.intern =

beta7 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

66608

name =

beta8

short.name =

beta8

output.name =

beta8 for ggaussianS observations

output.name.intern =

beta8 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

66609

name =

beta9

short.name =

beta9

output.name =

beta9 for ggaussianS observations

output.name.intern =

beta9 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

66610

name =

beta10

short.name =

beta10

output.name =

beta10 for ggaussianS observations

output.name.intern =

beta10 for ggaussianS observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 0.001

to.theta =

function(x) x

from.theta =

function(x) x

Model 'bcgaussian'.

Properties:

doc =

The Box-Cox Gaussian likelihoood

status =

disabled

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

bcgaussian

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

65010

name =

log precision

short.name =

prec

output.name =

Precision for the Box-Cox Gaussian observations

output.name.intern =

Log precision for the Box-Cox Gaussian observations

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

65011

name =

Box-Cox transformation parameter

short.name =

lambda

output.name =

NOT IN USE

output.name.intern =

NOT IN USE

initial =

1

fixed =

FALSE

prior =

gaussian

param =

1 8

to.theta =

function(x) x

from.theta =

function(x) x

Model 'exppower'.

Properties:

doc =

The exponential power likelihoood

survival =

FALSE

discrete =

FALSE

link =

default identity quantile

pdf =

exppower

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

65021

name =

log precision

short.name =

prec

output.name =

NOT IN USE

output.name.intern =

NOT IN USE

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

65022

name =

power

short.name =

beta

output.name =

NOT IN USE

output.name.intern =

NOT IN USE

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 100

to.theta =

function(x) log(x-1)

from.theta =

function(x) 1+exp(x)

Model 'sem'.

Properties:

doc =

The SEM likelihoood

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

sem

Number of hyperparmeters is 0.

Model 'rcpoisson'.

Properties:

doc =

Randomly censored Poisson

status =

experimental

survival =

FALSE

discrete =

TRUE

link =

default log

pdf =

rcpoisson

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

66701

name =

beta1

short.name =

beta1

output.name =

beta1 rcpoisson observations

output.name.intern =

beta1 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

66702

name =

beta2

short.name =

beta2

output.name =

beta2 rcpoisson observations

output.name.intern =

beta2 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

66703

name =

beta3

short.name =

beta3

output.name =

beta3 rcpoisson observations

output.name.intern =

beta3 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

66704

name =

beta4

short.name =

beta4

output.name =

beta4 rcpoisson observations

output.name.intern =

beta4 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

66705

name =

beta5

short.name =

beta5

output.name =

beta5 rcpoisson observations

output.name.intern =

beta5 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

66706

name =

beta6

short.name =

beta6

output.name =

beta6 rcpoisson observations

output.name.intern =

beta6 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

66707

name =

beta7

short.name =

beta7

output.name =

beta7 rcpoisson observations

output.name.intern =

beta7 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

66708

name =

beta8

short.name =

beta8

output.name =

beta8 rcpoisson observations

output.name.intern =

beta8 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

66709

name =

beta9

short.name =

beta9

output.name =

beta9 rcpoisson observations

output.name.intern =

beta9 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

66710

name =

beta10

short.name =

beta10

output.name =

beta10 rcpoisson observations

output.name.intern =

beta10 rcpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'tpoisson'.

Properties:

doc =

Thinned Poisson

survival =

FALSE

discrete =

TRUE

link =

default log

pdf =

tpoisson

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

66721

name =

beta1

short.name =

beta1

output.name =

beta1 tpoisson observations

output.name.intern =

beta1 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

66722

name =

beta2

short.name =

beta2

output.name =

beta2 tpoisson observations

output.name.intern =

beta2 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

66723

name =

beta3

short.name =

beta3

output.name =

beta3 tpoisson observations

output.name.intern =

beta3 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

66724

name =

beta4

short.name =

beta4

output.name =

beta4 tpoisson observations

output.name.intern =

beta4 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

66725

name =

beta5

short.name =

beta5

output.name =

beta5 tpoisson observations

output.name.intern =

beta5 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

66726

name =

beta6

short.name =

beta6

output.name =

beta6 tpoisson observations

output.name.intern =

beta6 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

66727

name =

beta7

short.name =

beta7

output.name =

beta7 tpoisson observations

output.name.intern =

beta7 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

66728

name =

beta8

short.name =

beta8

output.name =

beta8 tpoisson observations

output.name.intern =

beta8 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

66729

name =

beta9

short.name =

beta9

output.name =

beta9 tpoisson observations

output.name.intern =

beta9 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

66730

name =

beta10

short.name =

beta10

output.name =

beta10 tpoisson observations

output.name.intern =

beta10 tpoisson observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'circularnormal'.

Properties:

doc =

The circular Gaussian likelihoood

survival =

FALSE

discrete =

FALSE

link =

default tan tan.pi

pdf =

circular-normal

status =

disabled

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

67001

name =

log precision parameter

short.name =

prec

output.name =

Precision parameter for the Circular Normal observations

output.name.intern =

Log precision parameter for the Circular Normal observations

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'wrappedcauchy'.

Properties:

doc =

The wrapped Cauchy likelihoood

survival =

FALSE

discrete =

FALSE

link =

default tan tan.pi

pdf =

wrapped-cauchy

status =

disabled

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

68001

name =

log precision parameter

short.name =

prec

output.name =

Precision parameter for the Wrapped Cauchy observations

output.name.intern =

Log precision parameter for the Wrapped Cauchy observations

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.005

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'iidgamma'.

Properties:

doc =

(experimental)

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

iidgamma

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

69001

name =

logshape

short.name =

shape

output.name =

Shape parameter for iid-gamma

output.name.intern =

Log shape parameter for iid-gamma

initial =

0

fixed =

FALSE

prior =

loggamma

param =

100 100

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

69002

name =

lograte

short.name =

rate

output.name =

Rate parameter for iid-gamma

output.name.intern =

Log rate parameter for iid-gamma

initial =

0

fixed =

FALSE

prior =

loggamma

param =

100 100

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'iidlogitbeta'.

Properties:

doc =

(experimental)

survival =

FALSE

discrete =

FALSE

link =

default logit loga

pdf =

iidlogitbeta

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

70001

name =

log.a

short.name =

a

output.name =

a parameter for iid-beta

output.name.intern =

Log a parameter for iid-beta

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

70002

name =

log.b

short.name =

b

output.name =

Rate parameter for iid-gamma

output.name.intern =

Log rate parameter for iid-gamma

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'loggammafrailty'.

Properties:

doc =

(experimental)

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

loggammafrailty

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

71001

name =

log precision

short.name =

prec

output.name =

precision for the gamma frailty

output.name.intern =

log precision for the gamma frailty

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'logistic'.

Properties:

doc =

The Logistic likelihoood

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

logistic

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

72001

name =

log precision

short.name =

prec

output.name =

precision for the logistic observations

output.name.intern =

log precision for the logistic observations

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'sn'.

Properties:

doc =

The Skew-Normal likelihoood

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

sn

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

74001

name =

log precision

short.name =

prec

output.name =

precision for skew-normal observations

output.name.intern =

log precision for skew-normal observations

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

74002

name =

logit skew

short.name =

skew

output.name =

Skewness for skew-normal observations

output.name.intern =

Intern skewness for skew-normal observations

initial =

0.00123456789

fixed =

FALSE

prior =

pc.sn

param =

10

to.theta =

function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))

from.theta =

function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)

Model 'gev'.

Properties:

doc =

The Generalized Extreme Value likelihood

survival =

FALSE

discrete =

FALSE

link =

default identity

status =

disabled: Use likelihood model 'bgev' instead; see inla.doc('bgev')

pdf =

gev

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

76001

name =

log precision

short.name =

prec

output.name =

precision for GEV observations

output.name.intern =

log precision for GEV observations

initial =

4

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

76002

name =

tail parameter

short.name =

tail

output.name =

tail parameter for GEV observations

output.name.intern =

tail parameter for GEV observations

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 25

to.theta =

function(x) x

from.theta =

function(x) x

Model 'lognormal'.

Properties:

doc =

The log-Normal likelihood

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

lognormal

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

77101

name =

log precision

short.name =

prec

output.name =

Precision for the lognormal observations

output.name.intern =

Log precision for the lognormal observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'lognormalsurv'.

Properties:

doc =

The log-Normal likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default identity

pdf =

lognormal

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

78001

name =

log precision

short.name =

prec

output.name =

Precision for the lognormalsurv observations

output.name.intern =

Log precision for the lognormalsurv observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

78002

name =

beta1

short.name =

beta1

output.name =

beta1 for logNormal-Cure

output.name.intern =

beta1 for logNormal-Cure

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

78003

name =

beta2

short.name =

beta2

output.name =

beta2 for logNormal-Cure

output.name.intern =

beta2 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

78004

name =

beta3

short.name =

beta3

output.name =

beta3 for logNormal-Cure

output.name.intern =

beta3 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

78005

name =

beta4

short.name =

beta4

output.name =

beta4 for logNormal-Cure

output.name.intern =

beta4 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

78006

name =

beta5

short.name =

beta5

output.name =

beta5 for logNormal-Cure

output.name.intern =

beta5 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

78007

name =

beta6

short.name =

beta6

output.name =

beta6 for logNormal-Cure

output.name.intern =

beta6 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

78008

name =

beta7

short.name =

beta7

output.name =

beta7 for logNormal-Cure

output.name.intern =

beta7 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

78009

name =

beta8

short.name =

beta8

output.name =

beta8 for logNormal-Cure

output.name.intern =

beta8 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

78010

name =

beta9

short.name =

beta9

output.name =

beta9 for logNormal-Cure

output.name.intern =

beta9 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

78011

name =

beta10

short.name =

beta10

output.name =

beta10 for logNormal-Cure

output.name.intern =

beta10 for logNormal-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'exponential'.

Properties:

doc =

The Exponential likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

exponential

Number of hyperparmeters is 0.

Model 'exponentialsurv'.

Properties:

doc =

The Exponential likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default log neglog

pdf =

exponential

Number of hyperparmeters is 10.

Hyperparameter 'theta1'

hyperid =

78020

name =

beta1

short.name =

beta1

output.name =

beta1 for Exp-Cure

output.name.intern =

beta1 for Exp-Cure

initial =

-4

fixed =

FALSE

prior =

normal

param =

-1 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

78021

name =

beta2

short.name =

beta2

output.name =

beta2 for Exp-Cure

output.name.intern =

beta2 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

78022

name =

beta3

short.name =

beta3

output.name =

beta3 for Exp-Cure

output.name.intern =

beta3 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

78023

name =

beta4

short.name =

beta4

output.name =

beta4 for Exp-Cure

output.name.intern =

beta4 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

78024

name =

beta5

short.name =

beta5

output.name =

beta5 for Exp-Cure

output.name.intern =

beta5 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

78025

name =

beta6

short.name =

beta6

output.name =

beta6 for Exp-Cure

output.name.intern =

beta6 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

78026

name =

beta7

short.name =

beta7

output.name =

beta7 for Exp-Cure

output.name.intern =

beta7 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

78027

name =

beta8

short.name =

beta8

output.name =

beta8 for Exp-Cure

output.name.intern =

beta8 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

78028

name =

beta9

short.name =

beta9

output.name =

beta9 for Exp-Cure

output.name.intern =

beta9 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

78029

name =

beta10

short.name =

beta10

output.name =

beta10 for Exp-Cure

output.name.intern =

beta10 for Exp-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'coxph'.

Properties:

doc =

Cox-proportional hazard likelihood

survival =

TRUE

discrete =

TRUE

link =

default log neglog

pdf =

coxph

Number of hyperparmeters is 0.

Model 'weibull'.

Properties:

doc =

The Weibull likelihood

survival =

FALSE

discrete =

FALSE

link =

default log neglog quantile

pdf =

weibull

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

79001

name =

log alpha

short.name =

alpha

output.name =

alpha parameter for weibull

output.name.intern =

alpha_intern for weibull

initial =

-2

fixed =

FALSE

prior =

pc.alphaw

param =

5

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Model 'weibullsurv'.

Properties:

doc =

The Weibull likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default log neglog quantile

pdf =

weibull

Number of hyperparmeters is 11.

Hyperparameter 'theta'

hyperid =

79101

name =

log alpha

short.name =

alpha

output.name =

alpha parameter for weibullsurv

output.name.intern =

alpha_intern for weibullsurv

initial =

-2

fixed =

FALSE

prior =

pc.alphaw

param =

5

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Hyperparameter 'theta2'

hyperid =

79102

name =

beta1

short.name =

beta1

output.name =

beta1 for Weibull-Cure

output.name.intern =

beta1 for Weibull-Cure

initial =

-7

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

79103

name =

beta2

short.name =

beta2

output.name =

beta2 for Weibull-Cure

output.name.intern =

beta2 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

79104

name =

beta3

short.name =

beta3

output.name =

beta3 for Weibull-Cure

output.name.intern =

beta3 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

79105

name =

beta4

short.name =

beta4

output.name =

beta4 for Weibull-Cure

output.name.intern =

beta4 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

79106

name =

beta5

short.name =

beta5

output.name =

beta5 for Weibull-Cure

output.name.intern =

beta5 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

79107

name =

beta6

short.name =

beta6

output.name =

beta6 for Weibull-Cure

output.name.intern =

beta6 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

79108

name =

beta7

short.name =

beta7

output.name =

beta7 for Weibull-Cure

output.name.intern =

beta7 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

79109

name =

beta8

short.name =

beta8

output.name =

beta8 for Weibull-Cure

output.name.intern =

beta8 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

79110

name =

beta9

short.name =

beta9

output.name =

beta9 for Weibull-Cure

output.name.intern =

beta9 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

79111

name =

beta10

short.name =

beta10

output.name =

beta10 for Weibull-Cure

output.name.intern =

beta10 for Weibull-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'loglogistic'.

Properties:

doc =

The loglogistic likelihood

survival =

FALSE

discrete =

FALSE

link =

default log neglog

pdf =

loglogistic

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

80001

name =

log alpha

short.name =

alpha

output.name =

alpha for loglogistic observations

output.name.intern =

log alpha for loglogistic observations

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'loglogisticsurv'.

Properties:

doc =

The loglogistic likelihood (survival)

survival =

TRUE

discrete =

FALSE

link =

default log neglog

pdf =

loglogistic

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

80011

name =

log alpha

short.name =

alpha

output.name =

alpha for loglogisticsurv observations

output.name.intern =

log alpha for loglogisticsurv observations

initial =

1

fixed =

FALSE

prior =

loggamma

param =

25 25

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

80012

name =

beta1

short.name =

beta1

output.name =

beta1 for logLogistic-Cure

output.name.intern =

beta1 for logLogistic-Cure

initial =

-5

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

80013

name =

beta2

short.name =

beta2

output.name =

beta2 for logLogistic-Cure

output.name.intern =

beta2 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

80014

name =

beta3

short.name =

beta3

output.name =

beta3 for logLogistic-Cure

output.name.intern =

beta3 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

80015

name =

beta4

short.name =

beta4

output.name =

beta4 for logLogistic-Cure

output.name.intern =

beta4 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

80016

name =

beta5

short.name =

beta5

output.name =

beta5 for logLogistic-Cure

output.name.intern =

beta5 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

80017

name =

beta6

short.name =

beta6

output.name =

beta6 for logLogistic-Cure

output.name.intern =

beta6 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

80018

name =

beta7

short.name =

beta7

output.name =

beta7 for logLogistic-Cure

output.name.intern =

beta7 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

80019

name =

beta8

short.name =

beta8

output.name =

beta8 for logLogistic-Cure

output.name.intern =

beta8 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

80020

name =

beta9

short.name =

beta9

output.name =

beta9 for logLogistic-Cure

output.name.intern =

beta9 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

80021

name =

beta10

short.name =

beta10

output.name =

beta10 for logLogistic-Cure

output.name.intern =

beta10 for logLogistic-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'stochvol'.

Properties:

doc =

The Gaussian stochvol likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

stochvolgaussian

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

82001

name =

log precision

short.name =

prec

output.name =

Offset precision for stochvol

output.name.intern =

Log offset precision for stochvol

initial =

500

fixed =

TRUE

prior =

loggamma

param =

1 0.005

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'stochvolln'.

Properties:

doc =

The Log-Normal stochvol likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

stochvolln

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

82011

name =

offset

short.name =

c

output.name =

Mean offset for stochvolln

output.name.intern =

Mean offset for stochvolln

initial =

0

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'stochvolsn'.

Properties:

doc =

The SkewNormal stochvol likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

stochvolsn

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

82101

name =

logit skew

short.name =

skew

output.name =

Skewness for stochvol_sn observations

output.name.intern =

Intern skewness for stochvol_sn observations

initial =

0.00123456789

fixed =

FALSE

prior =

pc.sn

param =

10

to.theta =

function(x, skew.max = 0.988) log((1 + x / skew.max) / (1 - x / skew.max))

from.theta =

function(x, skew.max = 0.988) skew.max * (2 * exp(x) / (1 + exp(x)) - 1)

Hyperparameter 'theta2'

hyperid =

82102

name =

log precision

short.name =

prec

output.name =

Offset precision for stochvol_sn

output.name.intern =

Log offset precision for stochvol_sn

initial =

500

fixed =

TRUE

prior =

loggamma

param =

1 0.005

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'stochvolt'.

Properties:

doc =

The Student-t stochvol likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

stochvolt

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

83001

name =

log degrees of freedom

short.name =

dof

output.name =

degrees of freedom for stochvol student-t

output.name.intern =

dof_intern for stochvol student-t

initial =

4

fixed =

FALSE

prior =

pc.dof

param =

15 0.5

to.theta =

function(x) log(x - 2)

from.theta =

function(x) 2 + exp(x)

Model 'stochvolnig'.

Properties:

doc =

The Normal inverse Gaussian stochvol likelihood

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

stochvolnig

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

84001

name =

skewness

short.name =

skew

output.name.intern =

skewness_param_intern for stochvol-nig

output.name =

skewness parameter for stochvol-nig

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

84002

name =

shape

short.name =

shape

output.name =

shape parameter for stochvol-nig

output.name.intern =

shape_param_intern for stochvol-nig

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 0.5

to.theta =

function(x) log(x - 1)

from.theta =

function(x) 1 + exp(x)

Model 'zeroinflatedpoisson0'.

Properties:

doc =

Zero-inflated Poisson, type 0

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

85001

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated poisson_0

output.name.intern =

intern zero-probability parameter for zero-inflated poisson_0

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedpoisson1'.

Properties:

doc =

Zero-inflated Poisson, type 1

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

86001

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated poisson_1

output.name.intern =

intern zero-probability parameter for zero-inflated poisson_1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedpoisson2'.

Properties:

doc =

Zero-inflated Poisson, type 2

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

87001

name =

log alpha

short.name =

a

output.name =

zero-probability parameter for zero-inflated poisson_2

output.name.intern =

intern zero-probability parameter for zero-inflated poisson_2

initial =

0.693147180559945

fixed =

FALSE

prior =

gaussian

param =

0.693147180559945 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatedcenpoisson0'.

Properties:

doc =

Zero-inflated censored Poisson, type 0

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

87101

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated poisson_0

output.name.intern =

intern zero-probability parameter for zero-inflated poisson_0

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedcenpoisson1'.

Properties:

doc =

Zero-inflated censored Poisson, type 1

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

87201

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated poisson_1

output.name.intern =

intern zero-probability parameter for zero-inflated poisson_1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbetabinomial0'.

Properties:

doc =

Zero-inflated Beta-Binomial, type 0

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

zeroinflated

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

88001

name =

overdispersion

short.name =

rho

output.name =

rho for zero-inflated betabinomial_0

output.name.intern =

rho_intern for zero-inflated betabinomial_0

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

88002

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated betabinomial_0

output.name.intern =

intern zero-probability parameter for zero-inflated betabinomial_0

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbetabinomial1'.

Properties:

doc =

Zero-inflated Beta-Binomial, type 1

survival =

FALSE

discrete =

TRUE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

zeroinflated

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

89001

name =

overdispersion

short.name =

rho

output.name =

rho for zero-inflated betabinomial_1

output.name.intern =

rho_intern for zero-inflated betabinomial_1

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 0.4

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

89002

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated betabinomial_1

output.name.intern =

intern zero-probability parameter for zero-inflated betabinomial_1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbinomial0'.

Properties:

doc =

Zero-inflated Binomial, type 0

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

90001

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated binomial_0

output.name.intern =

intern zero-probability parameter for zero-inflated binomial_0

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbinomial1'.

Properties:

doc =

Zero-inflated Binomial, type 1

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

91001

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated binomial_1

output.name.intern =

intern zero-probability parameter for zero-inflated binomial_1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatedbinomial2'.

Properties:

doc =

Zero-inflated Binomial, type 2

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

zeroinflated

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

92001

name =

alpha

short.name =

alpha

output.name =

zero-probability parameter for zero-inflated binomial_2

output.name.intern =

intern zero-probability parameter for zero-inflated binomial_2

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroninflatedbinomial2'.

Properties:

doc =

Zero and N inflated binomial, type 2

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

NA

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

93001

name =

alpha1

short.name =

alpha1

output.name =

alpha1 parameter for zero-n-inflated binomial_2

output.name.intern =

intern alpha1 parameter for zero-n-inflated binomial_2

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

93002

name =

alpha2

short.name =

alpha2

output.name =

alpha2 parameter for zero-n-inflated binomial_2

output.name.intern =

intern alpha2 parameter for zero-n-inflated binomial_2

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroninflatedbinomial3'.

Properties:

doc =

Zero and N inflated binomial, type 3

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

zeroinflated

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

93101

name =

alpha0

short.name =

alpha0

output.name =

alpha0 parameter for zero-n-inflated binomial_3

output.name.intern =

intern alpha0 parameter for zero-n-inflated binomial_3

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

93102

name =

alphaN

short.name =

alphaN

output.name.intern =

intern alphaN parameter for zero-n-inflated binomial_3

output.name =

alphaN parameter for zero-n-inflated binomial_3

initial =

1

fixed =

FALSE

prior =

loggamma

param =

1 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatedbetabinomial2'.

Properties:

doc =

Zero inflated Beta-Binomial, type 2

survival =

FALSE

discrete =

FALSE

link =

default logit loga cauchit probit cloglog ccloglog loglog robit sn

pdf =

zeroinflated

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

94001

name =

log alpha

short.name =

a

output.name =

zero-probability parameter for zero-inflated betabinomial_2

output.name.intern =

intern zero-probability parameter for zero-inflated betabinomial_2

initial =

0.693147180559945

fixed =

FALSE

prior =

gaussian

param =

0.693147180559945 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

94002

name =

beta

short.name =

b

output.name =

overdispersion parameter for zero-inflated betabinomial_2

output.name.intern =

intern overdispersion parameter for zero-inflated betabinomial_2

initial =

0

fixed =

FALSE

prior =

gaussian

param =

0 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatednbinomial0'.

Properties:

doc =

Zero inflated negBinomial, type 0

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

95001

name =

log size

short.name =

size

output.name =

size for nbinomial_0 zero-inflated observations

output.name.intern =

log size for nbinomial_0 zero-inflated observations

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

95002

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated nbinomial_0

output.name.intern =

intern zero-probability parameter for zero-inflated nbinomial_0

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatednbinomial1'.

Properties:

doc =

Zero inflated negBinomial, type 1

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

96001

name =

log size

short.name =

size

output.name =

size for nbinomial_1 zero-inflated observations

output.name.intern =

log size for nbinomial_1 zero-inflated observations

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

96002

name =

logit probability

short.name =

prob

output.name =

zero-probability parameter for zero-inflated nbinomial_1

output.name.intern =

intern zero-probability parameter for zero-inflated nbinomial_1

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatednbinomial1strata2'.

Properties:

doc =

Zero inflated negBinomial, type 1, strata 2

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

97001

name =

log size

short.name =

size

output.name =

size for zero-inflated nbinomial_1_strata2

output.name.intern =

log size for zero-inflated nbinomial_1_strata2

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

97002

name =

logit probability 1

short.name =

prob1

output.name =

zero-probability1 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability1 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta3'

hyperid =

97003

name =

logit probability 2

short.name =

prob2

output.name =

zero-probability2 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability2 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta4'

hyperid =

97004

name =

logit probability 3

short.name =

prob3

output.name =

zero-probability3 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability3 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta5'

hyperid =

97005

name =

logit probability 4

short.name =

prob4

output.name =

zero-probability4 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability4 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta6'

hyperid =

97006

name =

logit probability 5

short.name =

prob5

output.name =

zero-probability5 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability5 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta7'

hyperid =

97007

name =

logit probability 6

short.name =

prob6

output.name =

zero-probability6 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability6 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta8'

hyperid =

97008

name =

logit probability 7

short.name =

prob7

output.name =

zero-probability7 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability7 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta9'

hyperid =

97009

name =

logit probability 8

short.name =

prob8

output.name =

zero-probability8 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability8 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta10'

hyperid =

97010

name =

logit probability 9

short.name =

prob9

output.name =

zero-probability9 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability9 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta11'

hyperid =

97011

name =

logit probability 10

short.name =

prob10

output.name =

zero-probability10 for zero-inflated nbinomial_1_strata2

output.name.intern =

intern zero-probability10 for zero-inflated nbinomial_1_strata2

initial =

-1

fixed =

TRUE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Model 'zeroinflatednbinomial1strata3'.

Properties:

doc =

Zero inflated negBinomial, type 1, strata 3

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

98001

name =

logit probability

short.name =

prob

output.name =

zero-probability for zero-inflated nbinomial_1_strata3

output.name.intern =

intern zero-probability for zero-inflated nbinomial_1_strata3

initial =

-1

fixed =

FALSE

prior =

gaussian

param =

-1 0.2

to.theta =

function(x) log(x / (1 - x))

from.theta =

function(x) exp(x) / (1 + exp(x))

Hyperparameter 'theta2'

hyperid =

98002

name =

log size 1

short.name =

size1

output.name =

size1 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size1 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

98003

name =

log size 2

short.name =

size2

output.name =

size2 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size2 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

98004

name =

log size 3

short.name =

size3

output.name =

size3 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size3 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

98005

name =

log size 4

short.name =

size4

output.name =

size4 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size4 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

98006

name =

log size 5

short.name =

size5

output.name =

size5 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size5 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

98007

name =

log size 6

short.name =

size6

output.name =

size6 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size6 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

98008

name =

log size 7

short.name =

size7

output.name =

size7 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size7 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

98009

name =

log size 8

short.name =

size8

output.name =

size8 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size8 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

98010

name =

log size 9

short.name =

size9

output.name =

size9 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size9 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta11'

hyperid =

98011

name =

log size 10

short.name =

size10

output.name =

size10 for zero-inflated nbinomial_1_strata3

output.name.intern =

log_size10 for zero-inflated nbinomial_1_strata3

initial =

2.30258509299405

fixed =

TRUE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'zeroinflatednbinomial2'.

Properties:

doc =

Zero inflated negBinomial, type 2

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

zeroinflated

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

99001

name =

log size

short.name =

size

output.name =

size for nbinomial zero-inflated observations

output.name.inter =

log size for nbinomial zero-inflated observations

initial =

2.30258509299405

fixed =

FALSE

prior =

pc.mgamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

99002

name =

log alpha

short.name =

a

output.name =

parameter alpha for zero-inflated nbinomial2

output.name.intern =

parameter alpha.intern for zero-inflated nbinomial2

initial =

0.693147180559945

fixed =

FALSE

prior =

gaussian

param =

2 1

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 't'.

Properties:

doc =

Student-t likelihood

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

student-t

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

100001

name =

log precision

short.name =

prec

output.name =

precision for the student-t observations

output.name.intern =

log precision for the student-t observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

100002

name =

log degrees of freedom

short.name =

dof

output.name =

degrees of freedom for student-t

output.name.intern =

dof_intern for student-t

initial =

5

fixed =

FALSE

prior =

pc.dof

param =

15 0.5

to.theta =

function(x) log(x - 2)

from.theta =

function(x) 2 + exp(x)

Model 'tstrata'.

Properties:

doc =

A stratified version of the Student-t likelihood

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

tstrata

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

101001

name =

log degrees of freedom

short.name =

dof

output.name.intern =

dof_intern for tstrata

output.name =

degrees of freedom for tstrata

initial =

4

fixed =

FALSE

prior =

pc.dof

param =

15 0.5

to.theta =

function(x) log(x - 5)

from.theta =

function(x) 5 + exp(x)

Hyperparameter 'theta2'

hyperid =

101002

name =

log precision1

short.name =

prec1

output.name =

Prec for tstrata strata

output.name.intern =

Log prec for tstrata strata

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta3'

hyperid =

101003

name =

log precision2

short.name =

prec2

output.name =

Prec for tstrata strata[2]

output.name.intern =

Log prec for tstrata strata[2]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta4'

hyperid =

101004

name =

log precision3

short.name =

prec3

output.name =

Prec for tstrata strata[3]

output.name.intern =

Log prec for tstrata strata[3]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta5'

hyperid =

101005

name =

log precision4

short.name =

prec4

output.name =

Prec for tstrata strata[4]

output.name.intern =

Log prec for tstrata strata[4]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta6'

hyperid =

101006

name =

log precision5

short.name =

prec5

output.name =

Prec for tstrata strata[5]

output.name.intern =

Log prec for tstrata strata[5]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta7'

hyperid =

101007

name =

log precision6

short.name =

prec6

output.name =

Prec for tstrata strata[6]

output.name.intern =

Log prec for tstrata strata[6]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta8'

hyperid =

101008

name =

log precision7

short.name =

prec7

output.name =

Prec for tstrata strata[7]

output.name.intern =

Log prec for tstrata strata[7]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta9'

hyperid =

101009

name =

log precision8

short.name =

prec8

output.name =

Prec for tstrata strata[8]

output.name.intern =

Log prec for tstrata strata[8]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta10'

hyperid =

101010

name =

log precision9

short.name =

prec9

output.name =

Prec for tstrata strata[9]

output.name.intern =

Log prec for tstrata strata[9]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta11'

hyperid =

101011

name =

log precision10

short.name =

prec10

output.name =

Prec for tstrata strata[10]

output.name.intern =

Log prec for tstrata strata[10]

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'nmix'.

Properties:

doc =

Binomial-Poisson mixture

survival =

FALSE

discrete =

TRUE

link =

default logit loga probit

pdf =

nmix

Number of hyperparmeters is 15.

Hyperparameter 'theta1'

hyperid =

101101

name =

beta1

short.name =

beta1

output.name =

beta[1] for NMix observations

output.name.intern =

beta[1] for NMix observations

initial =

2.30258509299405

fixed =

FALSE

prior =

normal

param =

0 0.5

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

101102

name =

beta2

short.name =

beta2

output.name =

beta[2] for NMix observations

output.name.intern =

beta[2] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

101103

name =

beta3

short.name =

beta3

output.name =

beta[3] for NMix observations

output.name.intern =

beta[3] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

101104

name =

beta4

short.name =

beta4

output.name =

beta[4] for NMix observations

output.name.intern =

beta[4] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

101105

name =

beta5

short.name =

beta5

output.name =

beta[5] for NMix observations

output.name.intern =

beta[5] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

101106

name =

beta6

short.name =

beta6

output.name =

beta[6] for NMix observations

output.name.intern =

beta[6] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

101107

name =

beta7

short.name =

beta7

output.name =

beta[7] for NMix observations

output.name.intern =

beta[7] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

101108

name =

beta8

short.name =

beta8

output.name =

beta[8] for NMix observations

output.name.intern =

beta[8] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

101109

name =

beta9

short.name =

beta9

output.name =

beta[9] for NMix observations

output.name.intern =

beta[9] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

101110

name =

beta10

short.name =

beta10

output.name =

beta[10] for NMix observations

output.name.intern =

beta[10] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

101111

name =

beta11

short.name =

beta11

output.name =

beta[11] for NMix observations

output.name.intern =

beta[11] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

101112

name =

beta12

short.name =

beta12

output.name =

beta[12] for NMix observations

output.name.intern =

beta[12] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

101113

name =

beta13

short.name =

beta13

output.name =

beta[13] for NMix observations

output.name.intern =

beta[13] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

101114

name =

beta14

short.name =

beta14

output.name =

beta[14] for NMix observations

output.name.intern =

beta[14] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

101115

name =

beta15

short.name =

beta15

output.name =

beta[15] for NMix observations

output.name.intern =

beta[15] for NMix observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'nmixnb'.

Properties:

doc =

NegBinomial-Poisson mixture

survival =

FALSE

discrete =

TRUE

link =

default logit loga probit

pdf =

nmixnb

Number of hyperparmeters is 16.

Hyperparameter 'theta1'

hyperid =

101121

name =

beta1

short.name =

beta1

output.name =

beta[1] for NMixNB observations

output.name.intern =

beta[1] for NMixNB observations

initial =

2.30258509299405

fixed =

FALSE

prior =

normal

param =

0 0.5

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

101122

name =

beta2

short.name =

beta2

output.name =

beta[2] for NMixNB observations

output.name.intern =

beta[2] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

101123

name =

beta3

short.name =

beta3

output.name =

beta[3] for NMixNB observations

output.name.intern =

beta[3] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

101124

name =

beta4

short.name =

beta4

output.name =

beta[4] for NMixNB observations

output.name.intern =

beta[4] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

101125

name =

beta5

short.name =

beta5

output.name =

beta[5] for NMixNB observations

output.name.intern =

beta[5] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

101126

name =

beta6

short.name =

beta6

output.name =

beta[6] for NMixNB observations

output.name.intern =

beta[6] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

101127

name =

beta7

short.name =

beta7

output.name =

beta[7] for NMixNB observations

output.name.intern =

beta[7] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

101128

name =

beta8

short.name =

beta8

output.name =

beta[8] for NMixNB observations

output.name.intern =

beta[8] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

101129

name =

beta9

short.name =

beta9

output.name =

beta[9] for NMixNB observations

output.name.intern =

beta[9] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

101130

name =

beta10

short.name =

beta10

output.name =

beta[10] for NMixNB observations

output.name.intern =

beta[10] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

101131

name =

beta11

short.name =

beta11

output.name =

beta[11] for NMixNB observations

output.name.intern =

beta[11] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

101132

name =

beta12

short.name =

beta12

output.name =

beta[12] for NMixNB observations

output.name.intern =

beta[12] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

101133

name =

beta13

short.name =

beta13

output.name =

beta[13] for NMixNB observations

output.name.intern =

beta[13] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

101134

name =

beta14

short.name =

beta14

output.name =

beta[14] for NMixNB observations

output.name.intern =

beta[14] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

101135

name =

beta15

short.name =

beta15

output.name =

beta[15] for NMixNB observations

output.name.intern =

beta[15] for NMixNB observations

initial =

0

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

101136

name =

overdispersion

short.name =

overdispersion

output.name =

overdispersion for NMixNB observations

output.name.intern =

log_overdispersion for NMixNB observations

initial =

0

fixed =

FALSE

prior =

pc.gamma

param =

7

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'gp'.

Properties:

doc =

Generalized Pareto likelihood

survival =

FALSE

discrete =

TRUE

link =

default quantile

pdf =

genPareto

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

101201

name =

tail

short.name =

xi

output.name =

Tail parameter for the gp observations

output.name.intern =

Intern tail parameter for the gp observations

initial =

-4

fixed =

FALSE

prior =

pc.gevtail

param =

7 0 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Model 'egp'.

Properties:

doc =

Exteneded Generalized Pareto likelihood

survival =

FALSE

discrete =

FALSE

link =

default quantile

pdf =

egp

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

101211

name =

tail

short.name =

xi

output.name =

Tail parameter for egp observations

output.name.intern =

Intern tail parameter for egp observations

initial =

0

fixed =

FALSE

prior =

pc.egptail

param =

5 -0.5 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Hyperparameter 'theta2'

hyperid =

101212

name =

shape

short.name =

kappa

output.name =

Shape parameter for the egp observations

output.name.intern =

Intern shape parameter for the egp observations

initial =

0

fixed =

FALSE

prior =

loggamma

param =

100 100

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'dgp'.

Properties:

doc =

Discrete generalized Pareto likelihood

survival =

FALSE

discrete =

TRUE

link =

default quantile

pdf =

dgp

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

101301

name =

tail

short.name =

xi

output.name =

Tail parameter for the dgp observations

output.name.intern =

Intern tail parameter for the dgp observations

initial =

2

fixed =

FALSE

prior =

pc.gevtail

param =

7 0 0.5

to.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(REPLACE.ME.low, REPLACE.ME.high)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Model 'logperiodogram'.

Properties:

doc =

Likelihood for the log-periodogram

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

NA

Number of hyperparmeters is 0.

Model 'tweedie'.

Properties:

doc =

Tweedie distribution

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

tweedie

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

102101

name =

p

short.name =

p

output.name =

p parameter for Tweedie

output.name.intern =

p_intern parameter for Tweedie

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x, interval = c(1.0, 2.0)) log(-(interval[1] - x) / (interval[2] - x))

from.theta =

function(x, interval = c(1.0, 2.0)) interval[1] + (interval[2] - interval[1]) * exp(x) / (1.0 + exp(x))

Hyperparameter 'theta2'

hyperid =

102201

name =

dispersion

short.name =

phi

output.name =

Dispersion parameter for Tweedie

output.name.intern =

Log dispersion parameter for Tweedie

initial =

-4

fixed =

FALSE

prior =

loggamma

param =

100 100

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'fmri'.

Properties:

doc =

fmri distribution (special nc-chi)

survival =

FALSE

discrete =

FALSE

link =

default log

pdf =

fmri

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

103101

name =

precision

short.name =

prec

output.name =

Precision for fmri

output.name.intern =

Log precision for fmri

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

103202

name =

dof

short.name =

df

output.name =

NOT IN USE

output.name.intern =

NOT IN USE

initial =

4

fixed =

TRUE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'fmrisurv'.

Properties:

doc =

fmri distribution (special nc-chi)

survival =

TRUE

discrete =

FALSE

link =

default log

pdf =

fmri

Number of hyperparmeters is 2.

Hyperparameter 'theta1'

hyperid =

104101

name =

precision

short.name =

prec

output.name =

Precision for fmrisurv

output.name.intern =

Log precision for fmrisurv

initial =

0

fixed =

FALSE

prior =

loggamma

param =

10 10

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Hyperparameter 'theta2'

hyperid =

104201

name =

dof

short.name =

df

output.name =

NOT IN USE

output.name.intern =

NOT IN USE

initial =

4

fixed =

TRUE

prior =

normal

param =

0 1

to.theta =

function(x) x

from.theta =

function(x) x

Model 'gompertz'.

Properties:

doc =

gompertz distribution

survival =

FALSE

discrete =

FALSE

link =

default log neglog

pdf =

gompertz

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

105101

name =

shape

short.name =

alpha

output.name.intern =

alpha_intern for Gompertz

output.name =

alpha parameter for Gompertz

initial =

-1

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Model 'gompertzsurv'.

Properties:

doc =

gompertz distribution

survival =

TRUE

discrete =

FALSE

link =

default log neglog

pdf =

gompertz

Number of hyperparmeters is 11.

Hyperparameter 'theta1'

hyperid =

106101

name =

shape

short.name =

alpha

output.name.intern =

alpha_intern for Gompertz-surv

output.name =

alpha parameter for Gompertz-surv

initial =

-10

fixed =

FALSE

prior =

normal

param =

0 1

to.theta =

function(x, sc = 0.1) log(x) / sc

from.theta =

function(x, sc = 0.1) exp(sc * x)

Hyperparameter 'theta2'

hyperid =

106102

name =

beta1

short.name =

beta1

output.name =

beta1 for Gompertz-Cure

output.name.intern =

beta1 for Gompertz-Cure

initial =

-5

fixed =

FALSE

prior =

normal

param =

-4 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

106103

name =

beta2

short.name =

beta2

output.name =

beta2 for Gompertz-Cure

output.name.intern =

beta2 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

106104

name =

beta3

short.name =

beta3

output.name =

beta3 for Gompertz-Cure

output.name.intern =

beta3 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

106105

name =

beta4

short.name =

beta4

output.name =

beta4 for Gompertz-Cure

output.name.intern =

beta4 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

106106

name =

beta5

short.name =

beta5

output.name =

beta5 for Gompertz-Cure

output.name.intern =

beta5 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

106107

name =

beta6

short.name =

beta6

output.name =

beta6 for Gompertz-Cure

output.name.intern =

beta6 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

106108

name =

beta7

short.name =

beta7

output.name =

beta7 for Gompertz-Cure

output.name.intern =

beta7 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

106109

name =

beta8

short.name =

beta8

output.name =

beta8 for Gompertz-Cure

output.name.intern =

beta8 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

106110

name =

beta9

short.name =

beta9

output.name =

beta9 for Gompertz-Cure

output.name.intern =

beta9 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

106111

name =

beta10

short.name =

beta10

output.name =

beta10 for Gompertz-Cure

output.name.intern =

beta10 for Gompertz-Cure

initial =

0

fixed =

FALSE

prior =

normal

param =

0 100

to.theta =

function(x) x

from.theta =

function(x) x

Model 'dgompertzsurv'.

Properties:

doc =

destructive gompertz (survival) distribution

experimental =

TRUE

survival =

TRUE

discrete =

FALSE

link =

default log neglog

pdf =

dgompertz

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

108101

name =

shape

short.name =

alpha

output.name.intern =

alpha_intern for dGompertz

output.name =

alpha parameter for dGompertz

initial =

-1

fixed =

FALSE

prior =

normal

param =

0 10

to.theta =

function(x) x

from.theta =

function(x) x

Model 'vm'.

Properties:

doc =

von Mises circular distribution

experimental =

TRUE

status =

disabled

survival =

FALSE

discrete =

FALSE

link =

default circular tan identity

pdf =

vm

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

109101

name =

precision

short.name =

prec

output.name.intern =

prec_intern for vm

output.name =

precision parameter for vm

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'nvm'.

Properties:

doc =

Normal approx of the von Mises circular distribution

experimental =

TRUE

status =

disabled

survival =

FALSE

discrete =

FALSE

link =

default circular tan identity

pdf =

vm

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

109201

name =

precision

short.name =

prec

output.name.intern =

prec_intern for nvm

output.name =

precision parameter for nvm

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'lavm'.

Properties:

doc =

Link adjusted von Mises circular distribution

experimental =

TRUE

survival =

FALSE

discrete =

FALSE

link =

default circular tan identity

pdf =

lavm

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

109301

name =

precision

short.name =

prec

output.name.intern =

prec_intern for lavm

output.name =

precision parameter for lavm

initial =

2

fixed =

FALSE

prior =

loggamma

param =

1 0.01

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

Model 'cloglike'.

Properties:

doc =

User-defined likelihood

experimental =

TRUE

survival =

FALSE

discrete =

FALSE

link =

default identity

pdf =

cloglike

Number of hyperparmeters is 0.

'prior'

Valid models in this section are:

Model 'normal'.

Number of parameters in the prior = 2

Model 'gaussian'.

Number of parameters in the prior = 2

Model 'laplace'.

Number of parameters in the prior = 2

Model 'linksnintercept'.

Number of parameters in the prior = 2

Model 'wishart1d'.

Number of parameters in the prior = 2

Model 'wishart2d'.

Number of parameters in the prior = 4

Model 'wishart3d'.

Number of parameters in the prior = 7

Model 'wishart4d'.

Number of parameters in the prior = 11

Model 'wishart5d'.

Number of parameters in the prior = 16

Model 'loggamma'.

Number of parameters in the prior = 2

Model 'gamma'.

Number of parameters in the prior = 2

Model 'pc.prw2.range'.

Number of parameters in the prior = 4

Model 'minuslogsqrtruncnormal'.

Number of parameters in the prior = 2

Model 'logtnormal'.

Number of parameters in the prior = 2

Model 'logtgaussian'.

Number of parameters in the prior = 2

Model 'flat'.

Number of parameters in the prior = 0

Model 'logflat'.

Number of parameters in the prior = 0

Model 'logiflat'.

Number of parameters in the prior = 0

Model 'mvnorm'.

Number of parameters in the prior = -1

Model 'pc.alphaw'.

Number of parameters in the prior = 1

Model 'pc.ar'.

Number of parameters in the prior = 1

Model 'dirichlet'.

Number of parameters in the prior = 1

Model 'none'.

Number of parameters in the prior = 0

Model 'invalid'.

Number of parameters in the prior = 0

Model 'betacorrelation'.

Number of parameters in the prior = 2

Model 'logitbeta'.

Number of parameters in the prior = 2

Model 'pc.prec'.

Number of parameters in the prior = 2

Model 'pc.dof'.

Number of parameters in the prior = 2

Model 'pc.cor0'.

Number of parameters in the prior = 2

Model 'pc.cor1'.

Number of parameters in the prior = 2

Model 'pc.fgnh'.

Number of parameters in the prior = 2

Model 'pc.spde.GA'.

Number of parameters in the prior = 4

Model 'pc.matern'.

Number of parameters in the prior = 3

Model 'pc.range'.

Number of parameters in the prior = 2

Model 'pc.sn'.

Number of parameters in the prior = 1

Model 'pc.gamma'.

Number of parameters in the prior = 1

Model 'pc.mgamma'.

Number of parameters in the prior = 1

Model 'pc.gammacount'.

Number of parameters in the prior = 1

Model 'pc.gevtail'.

Number of parameters in the prior = 3

Model 'pc.egptail'.

Number of parameters in the prior = 3

Model 'pc'.

Number of parameters in the prior = 2

Model 'ref.ar'.

Number of parameters in the prior = 0

Model 'pom'.

Number of parameters in the prior = 0

Model 'jeffreystdf'.

Number of parameters in the prior = 0

Model 'wishartkd'.

Number of parameters in the prior = 301

Model 'expression:'.

Number of parameters in the prior = -1

Model 'table:'.

Number of parameters in the prior = -1

Model 'rprior:'.

Number of parameters in the prior = 0

'wrapper'

Valid models in this section are:

Model 'joint'.

Properties:

doc =

(experimental)

constr =

FALSE

nrow.ncol =

FALSE

augmented =

FALSE

aug.factor =

1

aug.constr =

NULL

n.div.by =

NULL

n.required =

FALSE

set.default.values =

FALSE

pdf =

NA

Number of hyperparmeters is 1.

Hyperparameter 'theta'

hyperid =

102001

name =

log precision

short.name =

prec

output.name =

NOT IN USE

output.name.intern =

NOT IN USE

initial =

0

fixed =

TRUE

prior =

loggamma

param =

1 5e-05

to.theta =

function(x) log(x)

from.theta =

function(x) exp(x)

'lp.scale'

Valid models in this section are:

Model 'lp.scale'.

Properties:

pdf =

lp.scale

Number of hyperparmeters is 100.

Hyperparameter 'theta1'

hyperid =

103001

name =

beta1

short.name =

b1

output.name =

beta[1] for lp_scale

output.name.intern =

beta[1] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta2'

hyperid =

103002

name =

beta2

short.name =

b2

output.name =

beta[2] for lp_scale

output.name.intern =

beta[2] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta3'

hyperid =

103003

name =

beta3

short.name =

b3

output.name =

beta[3] for lp_scale

output.name.intern =

beta[3] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta4'

hyperid =

103004

name =

beta4

short.name =

b4

output.name =

beta[4] for lp_scale

output.name.intern =

beta[4] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta5'

hyperid =

103005

name =

beta5

short.name =

b5

output.name =

beta[5] for lp_scale

output.name.intern =

beta[5] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta6'

hyperid =

103006

name =

beta6

short.name =

b6

output.name =

beta[6] for lp_scale

output.name.intern =

beta[6] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta7'

hyperid =

103007

name =

beta7

short.name =

b7

output.name =

beta[7] for lp_scale

output.name.intern =

beta[7] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta8'

hyperid =

103008

name =

beta8

short.name =

b8

output.name =

beta[8] for lp_scale

output.name.intern =

beta[8] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta9'

hyperid =

103009

name =

beta9

short.name =

b9

output.name =

beta[9] for lp_scale

output.name.intern =

beta[9] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta10'

hyperid =

103010

name =

beta10

short.name =

b10

output.name =

beta[10] for lp_scale

output.name.intern =

beta[10] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta11'

hyperid =

103011

name =

beta11

short.name =

b11

output.name =

beta[11] for lp_scale

output.name.intern =

beta[11] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta12'

hyperid =

103012

name =

beta12

short.name =

b12

output.name =

beta[12] for lp_scale

output.name.intern =

beta[12] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta13'

hyperid =

103013

name =

beta13

short.name =

b13

output.name =

beta[13] for lp_scale

output.name.intern =

beta[13] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta14'

hyperid =

103014

name =

beta14

short.name =

b14

output.name =

beta[14] for lp_scale

output.name.intern =

beta[14] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta15'

hyperid =

103015

name =

beta15

short.name =

b15

output.name =

beta[15] for lp_scale

output.name.intern =

beta[15] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta16'

hyperid =

103016

name =

beta16

short.name =

b16

output.name =

beta[16] for lp_scale

output.name.intern =

beta[16] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta17'

hyperid =

103017

name =

beta17

short.name =

b17

output.name =

beta[17] for lp_scale

output.name.intern =

beta[17] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta18'

hyperid =

103018

name =

beta18

short.name =

b18

output.name =

beta[18] for lp_scale

output.name.intern =

beta[18] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta19'

hyperid =

103019

name =

beta19

short.name =

b19

output.name =

beta[19] for lp_scale

output.name.intern =

beta[19] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta20'

hyperid =

103020

name =

beta20

short.name =

b20

output.name =

beta[20] for lp_scale

output.name.intern =

beta[20] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta21'

hyperid =

103021

name =

beta21

short.name =

b21

output.name =

beta[21] for lp_scale

output.name.intern =

beta[21] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta22'

hyperid =

103022

name =

beta22

short.name =

b22

output.name =

beta[22] for lp_scale

output.name.intern =

beta[22] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta23'

hyperid =

103023

name =

beta23

short.name =

b23

output.name =

beta[23] for lp_scale

output.name.intern =

beta[23] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta24'

hyperid =

103024

name =

beta24

short.name =

b24

output.name =

beta[24] for lp_scale

output.name.intern =

beta[24] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta25'

hyperid =

103025

name =

beta25

short.name =

b25

output.name =

beta[25] for lp_scale

output.name.intern =

beta[25] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta26'

hyperid =

103026

name =

beta26

short.name =

b26

output.name =

beta[26] for lp_scale

output.name.intern =

beta[26] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta27'

hyperid =

103027

name =

beta27

short.name =

b27

output.name =

beta[27] for lp_scale

output.name.intern =

beta[27] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta28'

hyperid =

103028

name =

beta28

short.name =

b28

output.name =

beta[28] for lp_scale

output.name.intern =

beta[28] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta29'

hyperid =

103029

name =

beta29

short.name =

b29

output.name =

beta[29] for lp_scale

output.name.intern =

beta[29] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta30'

hyperid =

103030

name =

beta30

short.name =

b30

output.name =

beta[30] for lp_scale

output.name.intern =

beta[30] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta31'

hyperid =

103031

name =

beta31

short.name =

b31

output.name =

beta[31] for lp_scale

output.name.intern =

beta[31] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta32'

hyperid =

103032

name =

beta32

short.name =

b32

output.name =

beta[32] for lp_scale

output.name.intern =

beta[32] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta33'

hyperid =

103033

name =

beta33

short.name =

b33

output.name =

beta[33] for lp_scale

output.name.intern =

beta[33] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta34'

hyperid =

103034

name =

beta34

short.name =

b34

output.name =

beta[34] for lp_scale

output.name.intern =

beta[34] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta35'

hyperid =

103035

name =

beta35

short.name =

b35

output.name =

beta[35] for lp_scale

output.name.intern =

beta[35] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta36'

hyperid =

103036

name =

beta36

short.name =

b36

output.name =

beta[36] for lp_scale

output.name.intern =

beta[36] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta37'

hyperid =

103037

name =

beta37

short.name =

b37

output.name =

beta[37] for lp_scale

output.name.intern =

beta[37] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta38'

hyperid =

103038

name =

beta38

short.name =

b38

output.name =

beta[38] for lp_scale

output.name.intern =

beta[38] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta39'

hyperid =

103039

name =

beta39

short.name =

b39

output.name =

beta[39] for lp_scale

output.name.intern =

beta[39] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta40'

hyperid =

103040

name =

beta40

short.name =

b40

output.name =

beta[40] for lp_scale

output.name.intern =

beta[40] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta41'

hyperid =

103041

name =

beta41

short.name =

b41

output.name =

beta[41] for lp_scale

output.name.intern =

beta[41] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta42'

hyperid =

103042

name =

beta42

short.name =

b42

output.name =

beta[42] for lp_scale

output.name.intern =

beta[42] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta43'

hyperid =

103043

name =

beta43

short.name =

b43

output.name =

beta[43] for lp_scale

output.name.intern =

beta[43] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta44'

hyperid =

103044

name =

beta44

short.name =

b44

output.name =

beta[44] for lp_scale

output.name.intern =

beta[44] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta45'

hyperid =

103045

name =

beta45

short.name =

b45

output.name =

beta[45] for lp_scale

output.name.intern =

beta[45] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta46'

hyperid =

103046

name =

beta46

short.name =

b46

output.name =

beta[46] for lp_scale

output.name.intern =

beta[46] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta47'

hyperid =

103047

name =

beta47

short.name =

b47

output.name =

beta[47] for lp_scale

output.name.intern =

beta[47] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta48'

hyperid =

103048

name =

beta48

short.name =

b48

output.name =

beta[48] for lp_scale

output.name.intern =

beta[48] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta49'

hyperid =

103049

name =

beta49

short.name =

b49

output.name =

beta[49] for lp_scale

output.name.intern =

beta[49] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta50'

hyperid =

103050

name =

beta50

short.name =

b50

output.name =

beta[50] for lp_scale

output.name.intern =

beta[50] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta51'

hyperid =

103051

name =

beta51

short.name =

b51

output.name =

beta[51] for lp_scale

output.name.intern =

beta[51] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta52'

hyperid =

103052

name =

beta52

short.name =

b52

output.name =

beta[52] for lp_scale

output.name.intern =

beta[52] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta53'

hyperid =

103053

name =

beta53

short.name =

b53

output.name =

beta[53] for lp_scale

output.name.intern =

beta[53] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta54'

hyperid =

103054

name =

beta54

short.name =

b54

output.name =

beta[54] for lp_scale

output.name.intern =

beta[54] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta55'

hyperid =

103055

name =

beta55

short.name =

b55

output.name =

beta[55] for lp_scale

output.name.intern =

beta[55] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta56'

hyperid =

103056

name =

beta56

short.name =

b56

output.name =

beta[56] for lp_scale

output.name.intern =

beta[56] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta57'

hyperid =

103057

name =

beta57

short.name =

b57

output.name =

beta[57] for lp_scale

output.name.intern =

beta[57] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta58'

hyperid =

103058

name =

beta58

short.name =

b58

output.name =

beta[58] for lp_scale

output.name.intern =

beta[58] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta59'

hyperid =

103059

name =

beta59

short.name =

b59

output.name =

beta[59] for lp_scale

output.name.intern =

beta[59] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta60'

hyperid =

103060

name =

beta60

short.name =

b60

output.name =

beta[60] for lp_scale

output.name.intern =

beta[60] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta61'

hyperid =

103061

name =

beta61

short.name =

b61

output.name =

beta[61] for lp_scale

output.name.intern =

beta[61] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta62'

hyperid =

103062

name =

beta62

short.name =

b62

output.name =

beta[62] for lp_scale

output.name.intern =

beta[62] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta63'

hyperid =

103063

name =

beta63

short.name =

b63

output.name =

beta[63] for lp_scale

output.name.intern =

beta[63] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta64'

hyperid =

103064

name =

beta64

short.name =

b64

output.name =

beta[64] for lp_scale

output.name.intern =

beta[64] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta65'

hyperid =

103065

name =

beta65

short.name =

b65

output.name =

beta[65] for lp_scale

output.name.intern =

beta[65] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta66'

hyperid =

103066

name =

beta66

short.name =

b66

output.name =

beta[66] for lp_scale

output.name.intern =

beta[66] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta67'

hyperid =

103067

name =

beta67

short.name =

b67

output.name =

beta[67] for lp_scale

output.name.intern =

beta[67] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta68'

hyperid =

103068

name =

beta68

short.name =

b68

output.name =

beta[68] for lp_scale

output.name.intern =

beta[68] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta69'

hyperid =

103069

name =

beta69

short.name =

b69

output.name =

beta[69] for lp_scale

output.name.intern =

beta[69] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta70'

hyperid =

103070

name =

beta70

short.name =

b70

output.name =

beta[70] for lp_scale

output.name.intern =

beta[70] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta71'

hyperid =

103071

name =

beta71

short.name =

b71

output.name =

beta[71] for lp_scale

output.name.intern =

beta[71] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta72'

hyperid =

103072

name =

beta72

short.name =

b72

output.name =

beta[72] for lp_scale

output.name.intern =

beta[72] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta73'

hyperid =

103073

name =

beta73

short.name =

b73

output.name =

beta[73] for lp_scale

output.name.intern =

beta[73] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta74'

hyperid =

103074

name =

beta74

short.name =

b74

output.name =

beta[74] for lp_scale

output.name.intern =

beta[74] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta75'

hyperid =

103075

name =

beta75

short.name =

b75

output.name =

beta[75] for lp_scale

output.name.intern =

beta[75] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta76'

hyperid =

103076

name =

beta76

short.name =

b76

output.name =

beta[76] for lp_scale

output.name.intern =

beta[76] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta77'

hyperid =

103077

name =

beta77

short.name =

b77

output.name =

beta[77] for lp_scale

output.name.intern =

beta[77] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta78'

hyperid =

103078

name =

beta78

short.name =

b78

output.name =

beta[78] for lp_scale

output.name.intern =

beta[78] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta79'

hyperid =

103079

name =

beta79

short.name =

b79

output.name =

beta[79] for lp_scale

output.name.intern =

beta[79] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta80'

hyperid =

103080

name =

beta80

short.name =

b80

output.name =

beta[80] for lp_scale

output.name.intern =

beta[80] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta81'

hyperid =

103081

name =

beta81

short.name =

b81

output.name =

beta[81] for lp_scale

output.name.intern =

beta[81] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta82'

hyperid =

103082

name =

beta82

short.name =

b82

output.name =

beta[82] for lp_scale

output.name.intern =

beta[82] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta83'

hyperid =

103083

name =

beta83

short.name =

b83

output.name =

beta[83] for lp_scale

output.name.intern =

beta[83] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta84'

hyperid =

103084

name =

beta84

short.name =

b84

output.name =

beta[84] for lp_scale

output.name.intern =

beta[84] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta85'

hyperid =

103085

name =

beta85

short.name =

b85

output.name =

beta[85] for lp_scale

output.name.intern =

beta[85] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta86'

hyperid =

103086

name =

beta86

short.name =

b86

output.name =

beta[86] for lp_scale

output.name.intern =

beta[86] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta87'

hyperid =

103087

name =

beta87

short.name =

b87

output.name =

beta[87] for lp_scale

output.name.intern =

beta[87] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta88'

hyperid =

103088

name =

beta88

short.name =

b88

output.name =

beta[88] for lp_scale

output.name.intern =

beta[88] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta89'

hyperid =

103089

name =

beta89

short.name =

b89

output.name =

beta[89] for lp_scale

output.name.intern =

beta[89] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta90'

hyperid =

103090

name =

beta90

short.name =

b90

output.name =

beta[90] for lp_scale

output.name.intern =

beta[90] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta91'

hyperid =

103091

name =

beta91

short.name =

b91

output.name =

beta[91] for lp_scale

output.name.intern =

beta[91] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta92'

hyperid =

103092

name =

beta92

short.name =

b92

output.name =

beta[92] for lp_scale

output.name.intern =

beta[92] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta93'

hyperid =

103093

name =

beta93

short.name =

b93

output.name =

beta[93] for lp_scale

output.name.intern =

beta[93] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta94'

hyperid =

103094

name =

beta94

short.name =

b94

output.name =

beta[94] for lp_scale

output.name.intern =

beta[94] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta95'

hyperid =

103095

name =

beta95

short.name =

b95

output.name =

beta[95] for lp_scale

output.name.intern =

beta[95] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta96'

hyperid =

103096

name =

beta96

short.name =

b96

output.name =

beta[96] for lp_scale

output.name.intern =

beta[96] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta97'

hyperid =

103097

name =

beta97

short.name =

b97

output.name =

beta[97] for lp_scale

output.name.intern =

beta[97] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta98'

hyperid =

103098

name =

beta98

short.name =

b98

output.name =

beta[98] for lp_scale

output.name.intern =

beta[98] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta99'

hyperid =

103099

name =

beta99

short.name =

b99

output.name =

beta[99] for lp_scale

output.name.intern =

beta[99] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Hyperparameter 'theta100'

hyperid =

103100

name =

beta100

short.name =

b100

output.name =

beta[100] for lp_scale

output.name.intern =

beta[100] for lp_scale

initial =

1

fixed =

FALSE

prior =

normal

param =

1 10

to.theta =

function(x) x

from.theta =

function(x) x

Examples

## How to set hyperparameters to pass as the argument 'hyper'. This
## format is compatible with the old style (using 'initial', 'fixed',
## 'prior', 'param'), but the new style using 'hyper' takes precedence
## over the old style. The two styles can also be mixed. The old style
## might be removed from the code in the future...

## Only a subset need to be given
hyper <- list(theta = list(initial = 2))
## The `name' can be used instead of 'theta', or 'theta1', 'theta2',...
hyper <- list(precision = list(initial = 2))
hyper <- list(precision = list(prior = "flat", param = numeric(0)))
hyper <- list(theta2 = list(initial = 3), theta1 = list(prior = "gaussian"))
## The 'short.name' can be used instead of 'name'
hyper <- list(rho = list(param = c(0, 1)))