Package index
-
negbin()ordinal()multinomial()dpm_aft()dpm()weibull_aft()loglogistic_aft()lognormal_aft()ph()gaussian_ls()Gamma_ls()zi_poisson()zi_negbin()Beta()ordbeta()tweedie()custom_family() - Response families for generalized BART
-
posterior_predict(<bartisan_fit>)posterior_epred(<bartisan_fit>)posterior_linpred(<bartisan_fit>)log_lik(<bartisan_fit>)simulate(<bartisan_fit>)fitted(<bartisan_fit>)residuals(<bartisan_fit>)weights(<bartisan_fit>)sigma(<bartisan_fit>)prior_summary(<bartisan_fit>)print(<bartisan_prior_summary>)loo(<bartisan_fit>)waic(<bartisan_fit>)kfold(<bartisan_fit>)pp_check(<bartisan_fit>)as_draws(<bartisan_fit>)r2_posterior(<bartisan_fit>)r2(<bartisan_fit>)model_performance(<bartisan_fit>) - Interfaces to other packages
-
formula(<bartisan_fit>)terms(<bartisan_fit>)model.frame(<bartisan_fit>)nobs(<bartisan_fit>)family(<bartisan_fit>)get_predict(<bartisan_fit>)get_group_names(<bartisan_fit>)get_coef(<bartisan_fit>)set_coef(<bartisan_fit>)get_vcov(<bartisan_fit>)get_data(<bartisan_fit>) - Counterfactual estimands with marginaleffects
-
bartisan() - Fit a generalized Bayesian additive regression trees (BART) model
-
bartisan_control() - Sampler and prior settings for
bartisan() -
bcf() - Bayesian causal forests
-
coef(<bartisan_fit>) - Varying coefficients
-
diagnose() - Check whether a fit converged and mixed
-
error_density()plot(<bartisan_error_density>) - Error distribution of a Dirichlet process mixture fit
-
estimate_effect()print(<bartisan_effect>)plot(<bartisan_effect>) - Causal effects from a fitted model
-
partial_dependence()print(<bartisan_partial>)plot(<bartisan_partial>)plot(<bartisan_fit>) - Partial dependence on one or two predictors
-
predict(<bartisan_fit>) - Predictions from a generalized BART model
-
print(<bartisan_fit>)summary(<bartisan_fit>)print(<summary.bartisan_fit>) - Summarize a generalized BART model
-
print(<bcf_fit>)plot(<bcf_fit>) - Methods for Bayesian causal forest fits
-
ranef(<bartisan_fit>) - Group intercepts from a random-effect term
-
rhc - Right heart catheterization in critically ill patients
-
variable_importance()plot(<bartisan_importance>) - How often each predictor is used
-
vc() - Give a predictor a varying coefficient