| Bayesian Regression Models using 'Stan' | brms-package brms |
| Add model fit criteria to model objects | add_criterion add_criterion.brmsfit |
| Add model fit criteria to model objects | add_ic add_ic.brmsfit add_ic<- add_loo add_waic |
| Add compiled 'rstan' models to 'brmsfit' objects | add_rstan_model |
| Additional Response Information | addition-terms cat cens dec index rate resp_bhaz resp_cat resp_cens resp_dec resp_index resp_mi resp_rate resp_se resp_subset resp_thres resp_trials resp_trunc resp_vint resp_vreal resp_weights se subset thres trials trunc vint vreal weights |
| Set up AR(p) correlation structures | ar |
| Set up ARMA(p,q) correlation structures | arma |
| Transform into a brmsprior object | as.brmsprior |
| Extract Posterior Draws | as.array.brmsfit as.data.frame.brmsfit as.matrix.brmsfit |
| (Deprecated) Extract posterior samples for use with the 'coda' package | as.mcmc as.mcmc.brmsfit |
| The Asymmetric Laplace Distribution | AsymLaplace dasym_laplace pasym_laplace qasym_laplace rasym_laplace |
| Autocorrelation structures | autocor-terms |
| (Deprecated) Extract Autocorrelation Objects | autocor autocor.brmsfit |
| Bayes Factors from Marginal Likelihoods | bayes_factor bayes_factor.brmsfit |
| Compute a Bayesian version of R-squared for regression models | bayes_R2 bayes_R2.brmsfit |
| The Beta-binomial Distribution | BetaBinomial dbeta_binomial pbeta_binomial rbeta_binomial |
| Log Marginal Likelihood via Bridge Sampling | bridge_sampler bridge_sampler.brmsfit |
| Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models | brm |
| Run the same 'brms' model on multiple datasets | brm_multiple |
| Special Family Functions for 'brms' Models | acat asym_laplace bernoulli Beta beta_binomial brmsfamily categorical cox cratio cumulative dirichlet exgaussian exponential frechet gen_extreme_value geometric hurdle_cumulative hurdle_gamma hurdle_lognormal hurdle_negbinomial hurdle_poisson logistic_normal lognormal multinomial negbinomial shifted_lognormal skew_normal sratio student von_mises weibull wiener xbeta zero_inflated_beta zero_inflated_beta_binomial zero_inflated_binomial zero_inflated_negbinomial zero_inflated_poisson zero_one_inflated_beta |
| Class 'brmsfit' of models fitted with the 'brms' package | brmsfit brmsfit-class |
| Set up a model formula for use in 'brms' | bf brmsformula |
| Linear and Non-linear formulas in 'brms' | acformula bf-helpers brmsformula-helpers lf nlf set_mecor set_nl set_rescor |
| Descriptions of 'brmshypothesis' Objects | brmshypothesis plot.brmshypothesis print.brmshypothesis |
| Parse Formulas of 'brms' Models | brmsterms brmsterms.brmsformula brmsterms.default brmsterms.mvbrmsformula parse_bf |
| Spatial conditional autoregressive (CAR) structures | car |
| Extract Model Coefficients | coef.brmsfit |
| Combine Models fitted with 'brms' | combine_models |
| Compare Information Criteria of Different Models | compare_ic |
| Display Conditional Effects of Predictors | conditional_effects conditional_effects.brmsfit marginal_effects marginal_effects.brmsfit plot.brms_conditional_effects |
| Display Smooth Terms | conditional_smooths conditional_smooths.brmsfit marginal_smooths marginal_smooths.brmsfit |
| Constant priors in 'brms' | constant |
| Extract Control Parameters of the NUTS Sampler | control_params control_params.brmsfit |
| (Deprecated) AR(p) correlation structure | cor_ar |
| (Deprecated) ARMA(p,q) correlation structure | cor_arma cor_arma-class |
| (Deprecated) Correlation structure classes for the 'brms' package | cor_brms cor_brms-class |
| (Deprecated) Spatial conditional autoregressive (CAR) structures | cor_car cor_icar |
| (Deprecated) Compound Symmetry (COSY) Correlation Structure | cor_cosy cor_cosy-class |
| (Deprecated) Fixed user-defined covariance matrices | cor_fixed cov_fixed |
| (Deprecated) MA(q) correlation structure | cor_ma |
| (Deprecated) Spatial simultaneous autoregressive (SAR) structures | cor_errorsar cor_lagsar cor_sar |
| Set up COSY correlation structures | cosy |
| Prior sensitivity: Create priorsense data | create_priorsense_data.brmsfit |
| Category Specific Predictors in 'brms' Models | cs cse |
| Custom Families in 'brms' Models | customfamily custom_family |
| Default priors for Bayesian models | default_prior get_prior |
| Default Priors for 'brms' Models | default_prior.default |
| Compute Density Ratios | density_ratio |
| Extract Diagnostic Quantities of 'brms' Models | diagnostic-quantities log_posterior log_posterior.brmsfit neff_ratio neff_ratio.brmsfit nuts_params nuts_params.brmsfit rhat rhat.brmsfit |
| The Dirichlet Distribution | ddirichlet Dirichlet rdirichlet |
| Transform 'brmsfit' to 'draws' objects | as_draws as_draws.brmsfit as_draws_array as_draws_array.brmsfit as_draws_df as_draws_df.brmsfit as_draws_list as_draws_list.brmsfit as_draws_matrix as_draws_matrix.brmsfit as_draws_rvars as_draws_rvars.brmsfit draws-brms |
| Index 'brmsfit' objects | and chains, draws-index-brms draws. Index iterations, nchains nchains.brmsfit ndraws ndraws.brmsfit niterations niterations.brmsfit nvariables nvariables.brmsfit variables variables, variables.brmsfit |
| Support Functions for 'emmeans' | emmeans-brms-helpers emm_basis.brmsfit recover_data.brmsfit |
| Epileptic seizure counts | epilepsy |
| The Exponentially Modified Gaussian Distribution | dexgaussian ExGaussian pexgaussian rexgaussian |
| Expose user-defined 'Stan' functions | expose_functions expose_functions.brmsfit |
| Exponential function plus one. | expp1 |
| Extract Model Family Objects | family.brmsfit |
| Fixed residual correlation (FCOR) structures | fcor |
| Expected Values of the Posterior Predictive Distribution | fitted.brmsfit |
| Extract Population-Level Estimates | fixef fixef.brmsfit |
| The Frechet Distribution | dfrechet Frechet pfrechet qfrechet rfrechet |
| The Generalized Extreme Value Distribution | dgen_extreme_value GenExtremeValue pgen_extreme_value qgen_extreme_value rgen_extreme_value |
| Draws of a Distributional Parameter | get_dpar |
| Projection Predictive Variable Selection: Get Reference Model | get_refmodel.brmsfit |
| Set up Gaussian process terms in 'brms' | gp |
| Set up basic grouping terms in 'brms' | gr |
| Regularized horseshoe priors in 'brms' | horseshoe |
| Hurdle Distributions | dhurdle_gamma dhurdle_lognormal dhurdle_negbinomial dhurdle_poisson Hurdle phurdle_gamma phurdle_lognormal phurdle_negbinomial phurdle_poisson |
| Non-Linear Hypothesis Testing | hypothesis hypothesis.brmsfit hypothesis.default |
| Clarity of inhaler instructions | inhaler |
| Scaled inverse logit-link | inv_logit_scaled |
| The Inverse Gaussian Distribution | dinv_gaussian InvGaussian pinv_gaussian rinv_gaussian |
| Checks if argument is a 'brmsfit' object | is.brmsfit |
| Checks if argument is a 'brmsfit_multiple' object | is.brmsfit_multiple |
| Checks if argument is a 'brmsformula' object | is.brmsformula |
| Checks if argument is a 'brmsprior' object | is.brmsprior |
| Checks if argument is a 'brmsterms' object | is.brmsterms |
| Check if argument is a correlation structure | is.cor_arma is.cor_brms is.cor_car is.cor_cosy is.cor_fixed is.cor_sar |
| Checks if argument is a 'mvbrmsformula' object | is.mvbrmsformula |
| Checks if argument is a 'mvbrmsterms' object | is.mvbrmsterms |
| Predictions from K-Fold Cross-Validation | kfold_predict |
| K-Fold Cross-Validation | kfold kfold.brmsfit |
| Infections in kidney patients | kidney |
| (Defunct) Set up a lasso prior in 'brms' | lasso |
| Interface to 'shinystan' | launch_shinystan launch_shinystan.brmsfit |
| Compute the Pointwise Log-Likelihood | logLik.brmsfit log_lik log_lik.brmsfit |
| The (Multivariate) Logistic Normal Distribution | dlogistic_normal LogisticNormal rlogistic_normal |
| Scaled logit-link | logit_scaled |
| Logarithm with a minus one offset. | logm1 |
| Model comparison with the 'loo' package | loo_compare loo_compare.brmsfit |
| Model averaging via stacking or pseudo-BMA weighting. | loo_model_weights loo_model_weights.brmsfit |
| Moment matching for efficient approximate leave-one-out cross-validation | loo_moment_match loo_moment_match.brmsfit loo_moment_match.loo |
| Compute Weighted Expectations Using LOO | loo_epred loo_epred.brmsfit loo_linpred loo_linpred.brmsfit loo_predict loo_predict.brmsfit loo_predictive_interval loo_predictive_interval.brmsfit |
| Compute a LOO-adjusted R-squared for regression models | loo_R2 loo_R2.brmsfit |
| Efficient approximate leave-one-out cross-validation (LOO) using subsampling | loo_subsample loo_subsample.brmsfit |
| Efficient approximate leave-one-out cross-validation (LOO) | LOO loo LOO.brmsfit loo.brmsfit |
| Cumulative Insurance Loss Payments | loss |
| Set up MA(q) correlation structures | ma |
| Prepare Fully Crossed Conditions | make_conditions |
| MCMC Plots Implemented in 'bayesplot' | mcmc_plot mcmc_plot.brmsfit stanplot stanplot.brmsfit |
| Predictors with Measurement Error in 'brms' Models | me |
| Predictors with Missing Values in 'brms' Models | mi |
| Finite Mixture Families in 'brms' | mixture |
| Set up multi-membership grouping terms in 'brms' | mm |
| Multi-Membership Covariates | mmc |
| Monotonic Predictors in 'brms' Models | mo |
| Model Weighting Methods | model_weights model_weights.brmsfit |
| The Multivariate Normal Distribution | dmulti_normal MultiNormal rmulti_normal |
| The Multivariate Student-t Distribution | dmulti_student_t MultiStudentT rmulti_student_t |
| Bind response variables in multivariate models | mvbind |
| Set up a multivariate model formula for use in 'brms' | mvbf mvbrmsformula |
| Number of Grouping Factor Levels | ngrps ngrps.brmsfit |
| (Deprecated) Number of Posterior Samples | nsamples nsamples.brmsfit |
| GPU support in Stan via OpenCL | opencl |
| Create a matrix of output plots from a 'brmsfit' object | pairs.brmsfit |
| Extract Parameter Names | parnames parnames.brmsfit |
| Trace and Density Plots for MCMC Draws | plot.brmsfit |
| Posterior Model Probabilities from Marginal Likelihoods | post_prob post_prob.brmsfit |
| Posterior draws of parameters averaged across models | posterior_average posterior_average.brmsfit |
| Draws from the Expected Value of the Posterior Predictive Distribution | posterior_epred posterior_epred.brmsfit pp_expect |
| Compute posterior uncertainty intervals | posterior_interval posterior_interval.brmsfit |
| Posterior Draws of the Linear Predictor | posterior_linpred posterior_linpred.brmsfit |
| Draws from the Posterior Predictive Distribution | posterior_predict posterior_predict.brmsfit |
| (Deprecated) Extract Posterior Samples | posterior_samples posterior_samples.brmsfit |
| Posterior Predictions of Smooth Terms | posterior_smooths posterior_smooths.brmsfit |
| Summarize Posterior draws | posterior_summary posterior_summary.brmsfit posterior_summary.default |
| Table Creation for Posterior Draws | posterior_table |
| Posterior predictive draws averaged across models | pp_average pp_average.brmsfit |
| Posterior Predictive Checks for 'brmsfit' Objects | pp_check pp_check.brmsfit |
| Posterior Probabilities of Mixture Component Memberships | pp_mixture pp_mixture.brmsfit |
| Draws from the Posterior Predictive Distribution | predict.brmsfit |
| Posterior Draws of Predictive Errors | predictive_error predictive_error.brmsfit |
| Predictive Intervals | predictive_interval predictive_interval.brmsfit |
| Prepare Predictions | extract_draws prepare_predictions prepare_predictions.brmsfit |
| Print a summary for a fitted model represented by a 'brmsfit' object | print.brmsfit print.brmssummary |
| Print method for 'brmsprior' objects | print.brmsprior |
| Extract Prior Draws | prior_draws prior_draws.brmsfit prior_samples |
| Priors of 'brms' models | prior_summary prior_summary.brmsfit |
| Pareto smoothed importance sampling (PSIS) | psis psis.brmsfit |
| R2D2 Priors in 'brms' | R2D2 |
| Extract Group-Level Estimates | ranef ranef.brmsfit |
| Read CmdStan CSV files as a brms-formatted stanfit object | read_csv_as_stanfit |
| Recompile Stan models in 'brmsfit' objects | recompile_model |
| Compute exact cross-validation for problematic observations | reloo reloo.brmsfit reloo.loo |
| Rename parameters in brmsfit objects | rename_pars |
| Posterior Draws of Residuals/Predictive Errors | residuals.brmsfit |
| Restructure Old R Objects | restructure |
| Restructure Old 'brmsfit' Objects | restructure.brmsfit |
| Convert Rows to Labels | rows2labels |
| Defining smooths in 'brms' formulas | s t2 |
| Spatial simultaneous autoregressive (SAR) structures | sar |
| Control Saving of Parameter Draws | save_pars |
| Prior Definitions for 'brms' Models | brmsprior brmsprior-class empty_prior prior prior_ prior_string set_prior |
| The Shifted Log Normal Distribution | dshifted_lnorm pshifted_lnorm qshifted_lnorm rshifted_lnorm Shifted_Lognormal |
| The Skew-Normal Distribution | dskew_normal pskew_normal qskew_normal rskew_normal SkewNormal |
| Stan Code for Bayesian models | make_stancode stancode |
| Extract Stan code from 'brmsfit' objects | stancode.brmsfit |
| Stan Code for 'brms' Models | stancode.default |
| Stan data for Bayesian models | make_standata standata |
| Extract data passed to Stan from 'brmsfit' objects | standata.brmsfit |
| Data for 'brms' Models | standata.default |
| User-defined variables passed to Stan | stanvar stanvars |
| The Student-t Distribution | dstudent_t pstudent_t qstudent_t rstudent_t StudentT |
| Create a summary of a fitted model represented by a 'brmsfit' object | summary.brmsfit |
| (Deprecated) Black Theme for 'ggplot2' Graphics | theme_black |
| Default 'bayesplot' Theme for 'ggplot2' Graphics | theme_default |
| Threading in Stan | threading |
| Set up UNSTR correlation structures | unstr |
| Update Formula Addition Terms | update_adterms |
| Update 'brms' models | update.brmsfit |
| Update 'brms' models based on multiple data sets | update.brmsfit_multiple |
| Validate New Data | validate_newdata |
| Validate Prior for 'brms' Models | validate_prior |
| Extract Variance and Correlation Components | VarCorr VarCorr.brmsfit |
| Covariance and Correlation Matrix of Population-Level Effects | vcov.brmsfit |
| The von Mises Distribution | dvon_mises pvon_mises rvon_mises VonMises |
| Widely Applicable Information Criterion (WAIC) | WAIC waic WAIC.brmsfit waic.brmsfit |
| The Wiener Diffusion Model Distribution | dwiener rwiener Wiener |
| Zero-Inflated Distributions | dzero_inflated_beta dzero_inflated_beta_binomial dzero_inflated_binomial dzero_inflated_negbinomial dzero_inflated_poisson pzero_inflated_beta pzero_inflated_beta_binomial pzero_inflated_binomial pzero_inflated_negbinomial pzero_inflated_poisson ZeroInflated |
