Function reference • brms

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

Reference

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