inlaops

Serve inlabru models as versioned REST APIs that answer with posterior uncertainty, not just a point estimate.

Why

inlabru fits Bayesian spatial and latent Gaussian models well, but a fitted model lives in an R session. Getting its predictions to the people and systems that need them usually means re-running scripts by hand or exporting static tables.

inlaops connects inlabru to vetiver, the standard R tooling for model deployment. A bru fit can then be pinned with version history, served over HTTP and packaged in a container like any lm() or tidymodels fit. Each prediction carries the posterior mean, a 95% credible interval and the posterior standard deviation.

For example, inlabru’s gorilla nests article fits a log-Gaussian Cox process to nest locations in the Kagwene Gorilla Sanctuary. Pinned and served with inlaops, that intensity surface (~ exp(field + Intercept)) becomes an endpoint: a field team’s app, or a service in any language, sends coordinates to /predict and gets back the expected nest density there, with its credible interval. Nobody on the consuming side runs R. When the model is refitted on a new survey, the new version is pinned and served, and the old versions stay available for comparison and rollback.

Example

library(inlabru)
library(vetiver)
library(pins)
library(plumber)
library(inlaops)

# config = TRUE keeps what the API needs for posterior sampling.
fit <- bru(
  y ~ Intercept(1) + x1 + x2,
  family  = "gaussian",
  data    = dat,
  options = bru_options(control.compute = list(config = TRUE))
)

v <- vetiver_model(fit, "my_model",
  metadata = list(pred_formula = "~ Intercept + x1 + x2")
)

board <- board_folder("model-store")
vetiver_pin_write(board, v)

pr() |> vetiver_api(v) |> pr_run(port = 8088)

From any HTTP client:

curl -X POST http://127.0.0.1:8088/predict \
  -H "Content-Type: application/json" \
  -d '[{"x1": 0.5, "x2": 1.0}]'
#> [{".pred":1.99,".pred_low":1.88,".pred_high":2.11,".pred_sd":0.058}]

Point-referenced spatial fits (sf / sp points, SPDE fields, LGCP intensity surfaces) are served the same way: the request carries coordinates. Polygon / areal fits are not served yet.

inlaops also includes bru_reg(), a parsnip engine for fixed-effects inlabru models, and bru_score() for proper scoring rules (CRPS, log-score) on held-out data.

Installation

# INLA is not on CRAN
install.packages("INLA",
  repos = c(getOption("repos"), INLA = "https://inla.r-inla-download.org/R/stable")
)

# install.packages("pak")
pak::pak("novica/inlaops")