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: | 2.21.1 |
| 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), rstan (≥ 2.21.1), rstantools (≥ 2.1.0), shinystan (≥ 2.3.0), stats, survival (≥ 2.40.1), RcppParallel (≥ 5.0.1), utils |
| LinkingTo: | StanHeaders (≥ 2.21.0), rstan (≥ 2.21.1), BH (≥ 1.72.0-2), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1) |
| Suggests: | 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) |
| Published: | 2020-07-20 |
| 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 |
| CRAN checks: | rstanarm results |
| Reference manual: | rstanarm.pdf |
| Vignettes: |
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_2.21.1.tar.gz |
| Windows binaries: | r-devel: rstanarm_2.21.1.zip, r-release: rstanarm_2.21.1.zip, r-oldrel: rstanarm_2.21.1.zip |
| macOS binaries: | r-release: rstanarm_2.21.1.tgz, r-oldrel: rstanarm_2.19.2.tgz |
| Old sources: | rstanarm archive |
| Reverse depends: | evidence |
| Reverse imports: | embed, tidyposterior |
| Reverse suggests: | afex, bayesplot, BayesPostEst, bayestestR, bridgesampling, broom.mixed, concurve, correlation, effectsize, ggeffects, insight, loo, merTools, modelbased, parameters, performance, projpred, RBesT, see, shinybrms, shinystan, sjPlot, sjstats, tidybayes |
| Reverse enhances: | emmeans, interactions, jtools |
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