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 (≥, 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>
License: GPL (≥ 3)
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


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


Package source: rstanarm_2.32.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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: evidence, fbst
Reverse imports: BayesPostEst, bayesrules, eefAnalytics, IRexamples, jmBIG, tidyposterior, webSDM
Reverse suggests: afex, bayesMeanScale, bayesplot, bayestestR, bridgesampling, broom.helpers, broom.mixed, conformalbayes, correlation, datawizard, effectsize, embed, ggeffects, INLAjoint, insight, loo, marginaleffects, merTools, modelbased, modelsummary, parameters, performance, projpred, RBesT, report, SAMprior, see, shinybrms, shinystan, sjPlot, tidyAML, tidybayes
Reverse enhances: emmeans, interactions, jtools


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