Overview | Fixed-effect model | Grouped random effects | Classical SAE model connection | Structured process terms
This package is considered a duplicate. The official version of this package is found at:https://finleya.r-universe.dev/stLMM
Package: stLMM 0.0.3
Andrew O. Finley
stLMM: Bayesian Spatial and Space-Time Linear Mixed Models
Fits Bayesian linear mixed models for spatial and space-time data with fixed effects, independent and identically distributed (iid) grouped random effects, and structured latent processes. The formula interface supports first-order autoregressive (AR(1)) effects, dense Gaussian processes, nearest-neighbor Gaussian processes, proper and Leroux conditional autoregressive (CAR) effects, ordered directed acyclic graph autoregressive (DAGAR) effects, separable CAR-time and DAGAR-time effects, and spatially varying coefficients. The sampler uses sparse precision matrix calculations when available and includes post-fitting tools for latent process recovery, fitted values, prediction, pointwise log likelihoods, and posterior sample extraction. Method details include Datta et al. (2016) <doi:10.1080/01621459.2015.1044091>, Finley et al. (2019) <doi:10.1080/10618600.2018.1537924>, Datta et al. (2019) <doi:10.1214/19-BA1177>, and May and Finley (2025) <doi:10.1016/j.spasta.2025.100917>.
Authors:
stLMM_0.0.3.tar.gz
stLMM_0.0.3.zip(r-4.7-x86_64)stLMM_0.0.3.zip(r-4.7-arm64)stLMM_0.0.3.zip(r-4.6-x86_64)stLMM_0.0.3.zip(r-4.6-arm64)
stLMM_0.0.3.tar.gz(r-4.7-arm64)stLMM_0.0.3.tar.gz(r-4.7-x86_64)stLMM_0.0.3.tar.gz(r-4.6-arm64)stLMM_0.0.3.tar.gz(r-4.6-x86_64)
stLMM_0.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
stLMM/json (API)
| # Install 'stLMM' in R: |
| install.packages('stLMM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/finleya/stlmm/issues
Pkgdown/docs site:https://finleya.github.io
Uses libs:
Datasets:
- stunitco - FIA state, unit, and county polygons
Last updated from:a1c8b8d39f. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 183 | ||
| linux-devel-x86_64 | OK | 241 | ||
| source / vignettes | OK | 210 | ||
| linux-release-arm64 | OK | 213 | ||
| linux-release-x86_64 | OK | 227 | ||
| windows-devel-arm64 | OK | 393 | ||
| windows-devel-x86_64 | OK | 332 | ||
| windows-release-arm64 | OK | 373 | ||
| windows-release-x86_64 | OK | 284 | ||
| wasm-release | OK | 121 |
Exports:as_mcmcas_samplesbeta_distcar_graphfixedflatgamma_distget_cor_modelshalf_normalhalf_tiglog_liklog_normalnormalrecoverresidstLMMuniformwaic
Dependencies:BayesLogitcodalatticeMatrix
Getting started with stLMM
Last update: 2026-07-28
Started: 2026-07-28
Spatial NNGP models with stLMM
Overview | Model | Data | Fit | Recover | Predict | What this example illustrates | Full GP comparison
Last update: 2026-07-28
Started: 2026-07-28
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Convert stLMM samples to coda objects | as_mcmc as_mcmc.stLMM as_mcmc.stLMM_chains as_mcmc.stLMM_recovery as_mcmc.stLMM_recovery_chains |
| Collect stLMM posterior samples in a data frame | as_samples |
| Build a CAR adjacency graph | car_graph plot.stLMM_car_graph plot.stLMM_graph |
| Posterior fitted values for an stLMM fit | fitted.stLMM |
| List supported covariance models | get_cor_models |
| Pointwise log likelihood and WAIC | log_lik log_lik.stLMM log_lik.stLMM_chains log_lik.stLMM_recovery log_lik.stLMM_recovery_chains waic waic.default waic.stLMM waic.stLMM_chains waic.stLMM_recovery waic.stLMM_recovery_chains |
| Posterior prediction from recovered stLMM fits | predict.stLMM predict.stLMM_chains predict.stLMM_recovery predict.stLMM_recovery_chains |
| Recover latent process draws | recover recover.stLMM recover.stLMM_chains |
| Residual variance formula term | resid |
| Fit a Bayesian mixed model with structured latent processes | stLMM |
| Print, summarize, and plot stLMM objects | plot.stLMM plot.stLMM_chains plot.stLMM_prediction plot.stLMM_prediction_chains plot.stLMM_recovery plot.stLMM_recovery_chains print.stLMM print.stLMM_chains print.stLMM_prediction print.stLMM_recovery summary.stLMM summary.stLMM_chains summary.stLMM_prediction summary.stLMM_recovery summary.stLMM_recovery_chains |
| Prior constructors for stLMM | beta_dist fixed flat gamma_dist half_normal half_t ig log_normal normal stLMM-priors stLMM_priors uniform |
| stLMM formula terms | ar1 car car_time dagar dagar_time gp iid nngp stLMM-terms stLMM_terms |
| FIA state, unit, and county polygons | stunitco |