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brms
Bayesian Regression Models using 'Stan'
View on CRAN: Click
here
Download and install brms package within the R console
Install from CRAN:
install.packages("brms")
Install from Github:
library("remotes")
install_github("cran/brms") Install by package version:
library("remotes")
install_version("brms", "2.22.0") Attach the package and use:
library("brms")
Maintained by
Paul-Christian Bürkner
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-05-08
Latest Update: 2023-09-25
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 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: B
How to cite:
Paul-Christian Bürkner (2015). brms: Bayesian Regression Models using 'Stan'. R package version 2.22.0, https://cran.r-project.org/web/packages/brms. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.0 (2015-05-08 11:47), 0.2.0 (2015-05-27 01:35), 0.3.0 (2015-06-29 16:41), 0.4.0 (2015-07-23 15:24), 0.4.1 (2015-08-03 00:20), 0.5.0 (2015-09-13 09:22), 0.6.0 (2015-11-14 00:06), 0.7.0 (2016-01-18 13:17), 0.8.0 (2016-02-15 23:59), 0.9.0 (2016-04-19 13:50), 0.9.1 (2016-05-17 21:13), 0.10.0 (2016-06-29 17:03), 1.0.0 (2016-09-15 03:00), 1.0.1 (2016-09-16 12:50), 1.1.0 (2016-10-11 23:53), 1.2.0 (2016-12-06 11:28), 1.3.0 (2016-12-20 00:55), 1.3.1 (2016-12-22 00:38), 1.4.0 (2017-01-27 18:47), 1.5.0 (2017-02-17 19:02), 1.5.1 (2017-02-26 19:56), 1.6.0 (2017-04-06 23:05), 1.6.1 (2017-04-17 15:54), 1.7.0 (2017-05-23 19:29), 1.8.0 (2017-07-20 21:53), 1.9.0 (2017-08-15 14:18), 1.10.0 (2017-09-09 23:51), 1.10.2 (2017-10-20 22:52), 2.0.0 (2017-12-15 13:58), 2.0.1 (2017-12-21 22:24), 2.1.0 (2018-01-23 22:48), 2.2.0 (2018-04-13 10:43), 2.3.0 (2018-05-14 17:47), 2.3.1 (2018-06-05 19:43), 2.4.0 (2018-07-20 23:50), 2.5.0 (2018-09-16 18:40), 2.6.0 (2018-10-23 12:40), 2.7.0 (2018-12-17 17:00), 2.8.0 (2019-03-15 10:13), 2.9.0 (2019-05-23 07:00), 2.10.0 (2019-08-29 17:50), 2.11.0 (2020-01-12 15:50), 2.11.1 (2020-01-19 21:00), 2.12.0 (2020-02-23 18:30), 2.13.0 (2020-05-27 07:30), 2.13.3 (2020-07-13 15:10), 2.13.5 (2020-07-31 10:40), 2.14.0 (2020-10-08 16:20), 2.14.4 (2020-11-03 07:40), 2.15.0 (2021-03-14 16:50), 2.16.0 (2021-08-19 00:00), 2.16.1 (2021-08-23 16:00), 2.16.3 (2021-11-22 20:50), 2.17.0 (2022-04-13 16:22), 2.18.0 (2022-09-19 15:56), 2.19.0 (2023-03-14 16:40), 2.20.1 (2023-08-14 09:10), 2.20.3 (2023-09-15 19:12), 2.20.4 (2023-09-25 21:00), 2.21.0 (2024-03-20 13:30)
Other packages that cited brms R package
View brms citation profile
Other R packages that brms depends,
imports, suggests or enhances
Complete documentation for brms
Functions, R codes and Examples using
the brms R package
Some associated functions: AsymLaplace . BetaBinomial . Dirichlet . ExGaussian . Frechet . GenExtremeValue . Hurdle . InvGaussian . LogisticNormal . MultiNormal . MultiStudentT . R2D2 . Shifted_Lognormal . SkewNormal . StudentT . VarCorr.brmsfit . VonMises . Wiener . ZeroInflated . add_criterion . add_ic . add_rstan_model . addition-terms . ar . arma . as.brmsprior . as.data.frame.brmsfit . as.mcmc.brmsfit . autocor-terms . autocor.brmsfit . bayes_R2.brmsfit . bayes_factor.brmsfit . bridge_sampler.brmsfit . brm . brm_multiple . brms-package . brmsfamily . brmsfit-class . brmsfit_needs_refit . brmsformula-helpers . brmsformula . brmshypothesis . brmsterms . car . coef.brmsfit . combine_models . compare_ic . conditional_effects.brmsfit . conditional_smooths.brmsfit . control_params . cor_ar . cor_arma . cor_arr . cor_brms . cor_bsts . cor_car . cor_cosy . cor_fixed . cor_ma . cor_sar . cosy . cs . custom_family . data_predictor . data_response . density_ratio . diagnostic-quantities . do_call . draws-brms . draws-index-brms . emmeans-brms-helpers . epilepsy . expose_functions.brmsfit . expp1 . family.brmsfit . fcor . fitted.brmsfit . fixef.brmsfit . get_dpar . get_prior . get_refmodel.brmsfit . get_y . gp . gr . horseshoe . hypothesis.brmsfit . inhaler . inv_logit_scaled . is.brmsfit . is.brmsfit_multiple . is.brmsformula . is.brmsprior . is.brmsterms . is.cor_brms . is.mvbrmsformula . is.mvbrmsterms . kfold.brmsfit . kfold_predict . kidney . lasso . launch_shinystan.brmsfit . log_lik.brmsfit . logit_scaled . logm1 . loo.brmsfit . loo_R2.brmsfit . loo_compare.brmsfit . loo_model_weights.brmsfit . loo_moment_match.brmsfit . loo_predict.brmsfit . loo_subsample.brmsfit . loss . ma . make_conditions . make_stancode . make_standata . mcmc_plot.brmsfit . me . mi . mixture . mm . mmc . mo . model_weights.brmsfit . mvbind . mvbrmsformula . ngrps.brmsfit . nsamples.brmsfit . opencl . pairs.brmsfit . parnames . plot.brmsfit . post_prob.brmsfit . posterior_average.brmsfit . posterior_epred.brmsfit . posterior_interval.brmsfit . posterior_linpred.brmsfit . posterior_predict.brmsfit . posterior_samples.brmsfit . posterior_smooths.brmsfit . posterior_summary . posterior_table . pp_average.brmsfit . pp_check.brmsfit . pp_mixture.brmsfit . predict.brmsfit . predictive_error.brmsfit . predictive_interval.brmsfit . prepare_predictions . print.brmsfit . print.brmsprior . prior_draws.brmsfit . prior_summary.brmsfit . ranef.brmsfit . recompile_model . reloo.brmsfit . rename_pars . residuals.brmsfit . restructure . rows2labels . s . sar . save_pars . set_prior . stancode.brmsfit . standata.brmsfit . stanvar . summary.brmsfit . theme_black . theme_default . threading . unstr . update.brmsfit . update.brmsfit_multiple . update_adterms . validate_newdata . validate_prior . vcov.brmsfit . waic.brmsfit .
Some associated R codes: autocor.R . backends.R . bayes_R2.R . bridgesampling.R . brm.R . brm_multiple.R . brms-package.R . brmsfit-class.R . brmsfit-helpers.R . brmsfit-methods.R . brmsformula.R . brmsterms.R . conditional_effects.R . conditional_smooths.R . data-helpers.R . data-predictor.R . data-response.R . datasets.R . diagnostics.R . distributions.R . emmeans.R . exclude_pars.R . exclude_terms.R . families.R . family-lists.R . formula-ac.R . formula-ad.R . formula-cs.R . formula-gp.R . formula-re.R . formula-sm.R . formula-sp.R . ggplot-themes.R . hypothesis.R . kfold.R . launch_shinystan.R . log_lik.R . loo.R . loo_moment_match.R . loo_predict.R . loo_subsample.R . lsp.R . make_stancode.R . make_standata.R . misc.R . model_weights.R . numeric-helpers.R . plot.R . posterior.R . posterior_epred.R . posterior_predict.R . posterior_samples.R . posterior_smooths.R . pp_check.R . pp_mixture.R . predictive_error.R . predictor.R . prepare_predictions.R . prior_draws.R . priors.R . projpred.R . reloo.R . rename_pars.R . restructure.R . stan-helpers.R . stan-likelihood.R . stan-predictor.R . stan-prior.R . stan-response.R . stanvars.R . summary.R . update.R . zzz.R . Full brms package functions and examples
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