rstanarm: Bayesian Applied Regression Modeling via Stan

Estimates pre-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: 2.11.1
Depends: R (≥ 3.0.2), Rcpp (≥ 0.12.0), methods
Imports: ggplot2 (≥ 2.0.0), lme4 (≥ 1.1-8), loo (≥ 0.1.6), Matrix, nlme (≥ 3.1-124), rstan (≥ 2.11.1), shinystan (≥ 2.2.0), stats, utils
LinkingTo: StanHeaders (≥ 2.11.0), rstan (≥ 2.11.1), BH (≥ 1.60.0), Rcpp (≥ 0.12.0), RcppEigen
Suggests: arm, gamm4, gridExtra, HSAUR3, KernSmooth, knitr, MASS, rmarkdown, roxygen2, testthat (≥ 1.0.2)
Published: 2016-07-29
Author: Jonah Gabry [aut], Trustees of Columbia University [cph], R Core Deveopment Team [cph] (R/pp_data.R, 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), Ben Goodrich [cre, aut]
Maintainer: Ben Goodrich <benjamin.goodrich at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: NEWS
CRAN checks: rstanarm results


Reference manual: rstanarm.pdf
Vignettes: stan_aov: ANOVA Models
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_lm: Regularized Linear Models
stan_polr: Ordinal Models
Hierarchical Partial Pooling
How to Use the rstanarm Package
Package source: rstanarm_2.11.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: rstanarm_2.11.1.tgz, r-oldrel: rstanarm_2.11.1.tgz
Old sources: rstanarm archive

Reverse dependencies:

Reverse suggests: broom, shinystan