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paul-buerkner
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 both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, 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 prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bürkner. Last updated 2 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
1.3k stars 16.62 score 13k scripts 35 dependents
stan-dev
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.
Maintained by Ben Goodrich. Last updated 15 days ago.
bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmultilevel-modelsrstanrstanarmstanstatistical-modelingcpp
393 stars 15.70 score 5.0k scripts 13 dependentsstan-dev
rstantools:Tools for Developing R Packages Interfacing with 'Stan'
Provides various tools for developers of R packages interfacing with 'Stan' <https://mc-stan.org>, including functions to set up the required package structure, S3 generics and default methods to unify function naming across 'Stan'-based R packages, and vignettes with recommendations for developers.
Maintained by Jonah Gabry. Last updated 2 months ago.
bayesian-data-analysisbayesian-statisticsdeveloper-toolsstan
50 stars 13.13 score 134 scripts 225 dependents