| bh_beta_moments | Beta shapes from a prior mean and standard deviation |
| bh_control | Control Gibbs sampling |
| bh_diagnose | Diagnose computation and data-range information |
| bh_fit | Fit Bayesian bounded logistic regression |
| bh_log_lik | Pointwise log-likelihood draws |
| bh_posterior_draws | Extract posterior draws |
| bh_posterior_predict | Draw from the posterior predictive distribution |
| bh_prior | Specify priors for bounded logistic regression |
| bounded_probability | Bounded logistic probabilities |
| bounded_reg-methods | Methods for bounded logistic regression fits |
| coef.bounded_reg | Methods for bounded logistic regression fits |
| fitted.bounded_reg | Methods for bounded logistic regression fits |
| plot.bounded_reg | Plot a bounded logistic regression fit |
| predict.bounded_reg | Methods for bounded logistic regression fits |
| print.bh_control | Control Gibbs sampling |
| print.bh_diagnostic | Diagnose computation and data-range information |
| print.bh_prior | Specify priors for bounded logistic regression |
| print.bounded_reg | Methods for bounded logistic regression fits |
| print.summary.bounded_reg | Methods for bounded logistic regression fits |
| rbounded_binom | Generate bounded binomial outcomes |
| simulate.bounded_reg | Methods for bounded logistic regression fits |
| summary.bounded_reg | Methods for bounded logistic regression fits |