brms

Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

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Description file content

Package
brms
Encoding
UTF-8
Type
Package
Title
Bayesian Regression Models using 'Stan'
Version
2.3.1
Date
2018-05-29
Depends
R (>= 3.2.0), Rcpp (>= 0.12.0), ggplot2 (>= 2.0.0), methods
Imports
rstan (>= 2.17.2), loo (>= 2.0.0), Matrix (>= 1.1.1), mgcv (>= 1.8-13), rstantools (>= 1.3.0), bayesplot (>= 1.5.0), shinystan (>= 2.4.0), bridgesampling (>= 0.3-0), matrixStats, nleqslv, nlme, coda, abind, stats, utils, parallel, grDevices, backports
Suggests
testthat (>= 0.9.1), RWiener, future, mice, spdep, mnormt, lme4, MCMCglmm, ape, arm, statmod, digest, R.rsp, knitr, rmarkdown
Description
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: Bürkner (2017) ; Carpenter et al. (2017) .
LazyData
true
NeedsCompilation
no
License
GPL (>= 3)
URL
BugReports
https://github.com/paul-buerkner/brms/issues
VignetteBuilder
knitr, R.rsp
RoxygenNote
6.0.1
Packaged
2018-06-05 10:38:29 UTC; paulb
Author
Paul-Christian Bürkner [aut, cre]
Maintainer
Paul-Christian Bürkner
Repository
CRAN
Date/Publication
2018-06-05 17:43:01 UTC

install.packages('brms')

2.3.1

a month ago

https://github.com/paul-buerkner/brms

Paul-Christian Bürkner

GPL (>= 3)

Depends on

R (>= 3.2.0), Rcpp (>= 0.12.0), ggplot2 (>= 2.0.0), methods

Imports

rstan (>= 2.17.2), loo (>= 2.0.0), Matrix (>= 1.1.1), mgcv (>= 1.8-13), rstantools (>= 1.3.0), bayesplot (>= 1.5.0), shinystan (>= 2.4.0), bridgesampling (>= 0.3-0), matrixStats, nleqslv, nlme, coda, abind, stats, utils, parallel, grDevices, backports

Suggests

testthat (>= 0.9.1), RWiener, future, mice, spdep, mnormt, lme4, MCMCglmm, ape, arm, statmod, digest, R.rsp, knitr, rmarkdown

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