Package index • brms

All functions

dasym_laplace() pasym_laplace() qasym_laplace() rasym_laplace(): The Asymmetric Laplace Distribution
dbeta_binomial() pbeta_binomial() qbeta_binomial() rbeta_binomial(): The Beta-binomial Distribution
ddirichlet() rdirichlet(): The Dirichlet Distribution
dexgaussian() pexgaussian() qexgaussian() 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() qhurdle_poisson() rhurdle_poisson() dhurdle_negbinomial() phurdle_negbinomial() qhurdle_negbinomial() rhurdle_negbinomial() dhurdle_gamma() phurdle_gamma() qhurdle_gamma() rhurdle_gamma() dhurdle_lognormal() phurdle_lognormal() qhurdle_lognormal() rhurdle_lognormal(): Hurdle Distributions
dinv_gaussian() pinv_gaussian() qinv_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() qvon_mises() rvon_mises(): The von Mises Distribution
dwiener() rwiener(): The Wiener Diffusion Model Distribution
dzero_inflated_poisson() pzero_inflated_poisson() qzero_inflated_poisson() dzero_inflated_negbinomial() pzero_inflated_negbinomial() qzero_inflated_negbinomial() dzero_inflated_binomial() pzero_inflated_binomial() qzero_inflated_binomial() dzero_inflated_beta_binomial() pzero_inflated_beta_binomial() qzero_inflated_beta_binomial() dzero_inflated_beta() pzero_inflated_beta() qzero_inflated_beta(): Zero-Inflated Distributions
dzero_one_inflated_beta() pzero_one_inflated_beta() qzero_one_inflated_beta(): Zero-One-Inflated Beta Distribution
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_bhaz() 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() xbeta() 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() dirichlet_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
create_priorsense_data.brmsfit(): Prior sensitivity: Create priorsense data
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
inits(): Extract Initial Values Used for Each Chain
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>) loo_moment_match(<loo>): Moment matching for efficient approximate leave-one-out cross-validation
loo_predict(<brmsfit>) loo_epred() 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)