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rstanarm: Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Version: 2.32.1
Depends: R (≥ 3.4.0), Rcpp (≥ 0.12.0), methods
Imports: bayesplot (≥ 1.7.0), ggplot2 (≥ 2.2.1), lme4 (≥ 1.1-8), loo (≥ 2.1.0), Matrix (≥ 1.2-13), nlme (≥ 3.1-124), posterior, rstan (≥ 2.32.0), rstantools (≥ 2.1.0), shinystan (≥ 2.3.0), stats, survival (≥ 2.40.1), RcppParallel (≥ 5.0.1), utils
LinkingTo: StanHeaders (≥ 2.32.0), rstan (≥ 2.32.0), BH (≥ 1.72.0-2), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1)
Suggests: biglm, betareg, data.table (≥ 1.10.0), digest, gridExtra, HSAUR3, knitr (≥ 1.15.1), MASS, mgcv (≥ 1.8-13), rmarkdown, roxygen2, StanHeaders (≥ 2.21.0), testthat (≥ 1.0.2), gamm4, shiny, V8
Published: 2024-01-18
DOI: 10.32614/CRAN.package.rstanarm
Author: Jonah Gabry [aut], Imad Ali [ctb], Sam Brilleman [ctb], Jacqueline Buros Novik [ctb] (R/stan_jm.R), AstraZeneca [ctb] (R/stan_jm.R), Trustees of Columbia University [cph], Simon Wood [cph] (R/stan_gamm4.R), R Core Deveopment Team [cph] (R/stan_aov.R), Douglas Bates [cph] (R/pp_data.R), Martin Maechler [cph] (R/pp_data.R), Ben Bolker [cph] (R/pp_data.R), Steve Walker [cph] (R/pp_data.R), Brian Ripley [cph] (R/stan_aov.R, R/stan_polr.R), William Venables [cph] (R/stan_polr.R), Paul-Christian Burkner [cph] (R/misc.R), Ben Goodrich [cre, aut]
Maintainer: Ben Goodrich <benjamin.goodrich at columbia.edu>
BugReports: https://github.com/stan-dev/rstanarm/issues
License: GPL (≥ 3)
URL: https://mc-stan.org/rstanarm/, https://discourse.mc-stan.org
NeedsCompilation: yes
SystemRequirements: GNU make, pandoc (>= 1.12.3), pandoc-citeproc
Citation: rstanarm citation info
Materials: NEWS
In views: Bayesian, MixedModels, Survival
CRAN checks: rstanarm results [issues need fixing before 2025-09-03]

Documentation:

Reference manual: rstanarm.html , rstanarm.pdf
Vignettes: Probabilistic A/B Testing with rstanarm (source, R code)
stan_aov: ANOVA Models (source, R code)
stan_betareg: Models for Rate/Proportion Data (source, R code)
stan_glm: GLMs for Binary and Binomial Data (source, R code)
stan_glm: GLMs for Continuous Data (source, R code)
stan_glm: GLMs for Count Data (source, R code)
stan_glmer: GLMs with Group-Specific Terms (source, R code)
stan_jm: Joint Models for Longitudinal and Time-to-Event Data (source, R code)
stan_lm: Regularized Linear Models (source, R code)
MRP with rstanarm (source, R code)
stan_polr: Ordinal Models (source, R code)
Hierarchical Partial Pooling (source, R code)
Prior Distributions (source, R code)
How to Use the rstanarm Package (source, R code)

Downloads:

Package source: rstanarm_2.32.1.tar.gz
Windows binaries: r-devel: rstanarm_2.32.1.zip, r-release: rstanarm_2.32.1.zip, r-oldrel: rstanarm_2.32.1.zip
macOS binaries: r-release (arm64): rstanarm_2.32.1.tgz, r-oldrel (arm64): rstanarm_2.32.1.tgz, r-release (x86_64): rstanarm_2.32.1.tgz, r-oldrel (x86_64): rstanarm_2.32.1.tgz
Old sources: rstanarm archive

Reverse dependencies:

Reverse depends: AuxSurvey, evidence, fbst
Reverse imports: BayesERtools, BayesPostEst, bayesrules, IRexamples, JMbdirect, jmBIG, SIMPLE.REGRESSION, tidyposterior, webSDM
Reverse suggests: afex, bayesMeanScale, bayesplot, bayestestR, bridgesampling, broom.helpers, broom.mixed, correlation, datawizard, effectsize, embed, fastml, ggeffects, INLAjoint, insight, loo, marginaleffects, merTools, modelbased, modelsummary, orbital, parameters, performance, projpred, RBesT, rdss, report, SAMprior, see, shinybrms, shinystan, sjPlot, tidyAML, tidybayes
Reverse enhances: emmeans, interactions, jtools

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rstanarm to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.