| area_under_curve | Area under the Curve (AUC) | 
| as.data.frame.density | Coerce to a Data Frame | 
| as.logical.bayesfactor_restricted | Bayes Factors (BF) for Order Restricted Models | 
| as.matrix.bayesfactor_models | Bayes Factors (BF) for model comparison | 
| as.numeric.map_estimate | Convert to Numeric | 
| as.numeric.p_direction | Convert to Numeric | 
| as.numeric.p_map | Convert to Numeric | 
| as.numeric.p_significance | Convert to Numeric | 
| auc | Area under the Curve (AUC) | 
| bayesfactor | Bayes Factors (BF) | 
| bayesfactor_inclusion | Inclusion Bayes Factors for testing predictors across Bayesian models | 
| bayesfactor_models | Bayes Factors (BF) for model comparison | 
| bayesfactor_models.default | Bayes Factors (BF) for model comparison | 
| bayesfactor_parameters | Bayes Factors (BF) for a Single Parameter | 
| bayesfactor_parameters.data.frame | Bayes Factors (BF) for a Single Parameter | 
| bayesfactor_parameters.numeric | Bayes Factors (BF) for a Single Parameter | 
| bayesfactor_parameters.stanreg | Bayes Factors (BF) for a Single Parameter | 
| bayesfactor_pointnull | Bayes Factors (BF) for a Single Parameter | 
| bayesfactor_restricted | Bayes Factors (BF) for Order Restricted Models | 
| bayesfactor_restricted.blavaan | Bayes Factors (BF) for Order Restricted Models | 
| bayesfactor_restricted.brmsfit | Bayes Factors (BF) for Order Restricted Models | 
| bayesfactor_restricted.data.frame | Bayes Factors (BF) for Order Restricted Models | 
| bayesfactor_restricted.emmGrid | Bayes Factors (BF) for Order Restricted Models | 
| bayesfactor_restricted.stanreg | Bayes Factors (BF) for Order Restricted Models | 
| bayesfactor_rope | Bayes Factors (BF) for a Single Parameter | 
| bayesian_as_frequentist | Convert (refit) a Bayesian model to frequentist | 
| bcai | Bias Corrected and Accelerated Interval (BCa) | 
| bci | Bias Corrected and Accelerated Interval (BCa) | 
| bci.brmsfit | Bias Corrected and Accelerated Interval (BCa) | 
| bci.data.frame | Bias Corrected and Accelerated Interval (BCa) | 
| bci.get_predicted | Bias Corrected and Accelerated Interval (BCa) | 
| bci.numeric | Bias Corrected and Accelerated Interval (BCa) | 
| bf_inclusion | Inclusion Bayes Factors for testing predictors across Bayesian models | 
| bf_models | Bayes Factors (BF) for model comparison | 
| bf_parameters | Bayes Factors (BF) for a Single Parameter | 
| bf_pointnull | Bayes Factors (BF) for a Single Parameter | 
| bf_restricted | Bayes Factors (BF) for Order Restricted Models | 
| bf_rope | Bayes Factors (BF) for a Single Parameter | 
| bic_to_bf | Convert BIC indices to Bayes Factors via the BIC-approximation method. | 
| check_prior | Check if Prior is Informative | 
| check_prior.brmsfit | Check if Prior is Informative | 
| ci | Confidence/Credible/Compatibility Interval (CI) | 
| ci.brmsfit | Confidence/Credible/Compatibility Interval (CI) | 
| ci.data.frame | Confidence/Credible/Compatibility Interval (CI) | 
| ci.numeric | Confidence/Credible/Compatibility Interval (CI) | 
| contr.bayes | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation | 
| contr.equalprior | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation | 
| contr.equalprior_deviations | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation | 
| contr.equalprior_pairs | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation | 
| contr.orthonorm | Contrast Matrices for Equal Marginal Priors in Bayesian Estimation | 
| convert_bayesian_as_frequentist | Convert (refit) a Bayesian model to frequentist | 
| convert_pd_to_p | Convert between Probability of Direction (pd) and p-value. | 
| convert_p_to_pd | Convert between Probability of Direction (pd) and p-value. | 
| density_at | Density Probability at a Given Value | 
| describe_posterior | Describe Posterior Distributions | 
| describe_posterior.data.frame | Describe Posterior Distributions | 
| describe_posterior.numeric | Describe Posterior Distributions | 
| describe_posterior.stanreg | Describe Posterior Distributions | 
| describe_prior | Describe Priors | 
| describe_prior.brmsfit | Describe Priors | 
| diagnostic_draws | Diagnostic values for each iteration | 
| diagnostic_posterior | Posteriors Sampling Diagnostic | 
| diagnostic_posterior.default | Posteriors Sampling Diagnostic | 
| diagnostic_posterior.stanreg | Posteriors Sampling Diagnostic | 
| disgust | Moral Disgust Judgment | 
| distribution | Empirical Distributions | 
| distribution_beta | Empirical Distributions | 
| distribution_binom | Empirical Distributions | 
| distribution_binomial | Empirical Distributions | 
| distribution_cauchy | Empirical Distributions | 
| distribution_chisq | Empirical Distributions | 
| distribution_chisquared | Empirical Distributions | 
| distribution_custom | Empirical Distributions | 
| distribution_gamma | Empirical Distributions | 
| distribution_gaussian | Empirical Distributions | 
| distribution_mixture_normal | Empirical Distributions | 
| distribution_nbinom | Empirical Distributions | 
| distribution_normal | Empirical Distributions | 
| distribution_poisson | Empirical Distributions | 
| distribution_student | Empirical Distributions | 
| distribution_student_t | Empirical Distributions | 
| distribution_t | Empirical Distributions | 
| distribution_tweedie | Empirical Distributions | 
| distribution_uniform | Empirical Distributions | 
| effective_sample | Effective Sample Size (ESS) | 
| effective_sample.brmsfit | Effective Sample Size (ESS) | 
| equivalence_test | Test for Practical Equivalence | 
| equivalence_test.brmsfit | Test for Practical Equivalence | 
| equivalence_test.data.frame | Test for Practical Equivalence | 
| equivalence_test.default | Test for Practical Equivalence | 
| estimate_density | Density Estimation | 
| estimate_density.brmsfit | Density Estimation | 
| estimate_density.data.frame | Density Estimation | 
| eti | Equal-Tailed Interval (ETI) | 
| eti.brmsfit | Equal-Tailed Interval (ETI) | 
| eti.data.frame | Equal-Tailed Interval (ETI) | 
| eti.get_predicted | Equal-Tailed Interval (ETI) | 
| eti.numeric | Equal-Tailed Interval (ETI) | 
| hdi | Highest Density Interval (HDI) | 
| hdi.brmsfit | Highest Density Interval (HDI) | 
| hdi.data.frame | Highest Density Interval (HDI) | 
| hdi.get_predicted | Highest Density Interval (HDI) | 
| hdi.numeric | Highest Density Interval (HDI) | 
| map_estimate | Maximum A Posteriori probability estimate (MAP) | 
| map_estimate.brmsfit | Maximum A Posteriori probability estimate (MAP) | 
| map_estimate.data.frame | Maximum A Posteriori probability estimate (MAP) | 
| map_estimate.get_predicted | Maximum A Posteriori probability estimate (MAP) | 
| map_estimate.numeric | Maximum A Posteriori probability estimate (MAP) | 
| mcse | Monte-Carlo Standard Error (MCSE) | 
| mcse.stanreg | Monte-Carlo Standard Error (MCSE) | 
| mediation | Summary of Bayesian multivariate-response mediation-models | 
| mediation.brmsfit | Summary of Bayesian multivariate-response mediation-models | 
| model_to_priors | Convert model's posteriors to priors (EXPERIMENTAL) | 
| overlap | Overlap Coefficient | 
| pd | Probability of Direction (pd) | 
| pd_to_p | Convert between Probability of Direction (pd) and p-value. | 
| pd_to_p.numeric | Convert between Probability of Direction (pd) and p-value. | 
| point_estimate | Point-estimates of posterior distributions | 
| point_estimate.brmsfit | Point-estimates of posterior distributions | 
| point_estimate.data.frame | Point-estimates of posterior distributions | 
| point_estimate.get_predicted | Point-estimates of posterior distributions | 
| point_estimate.numeric | Point-estimates of posterior distributions | 
| p_direction | Probability of Direction (pd) | 
| p_direction.brmsfit | Probability of Direction (pd) | 
| p_direction.data.frame | Probability of Direction (pd) | 
| p_direction.get_predicted | Probability of Direction (pd) | 
| p_direction.numeric | Probability of Direction (pd) | 
| p_map | Bayesian p-value based on the density at the Maximum A Posteriori (MAP) | 
| p_map.brmsfit | Bayesian p-value based on the density at the Maximum A Posteriori (MAP) | 
| p_map.data.frame | Bayesian p-value based on the density at the Maximum A Posteriori (MAP) | 
| p_map.get_predicted | Bayesian p-value based on the density at the Maximum A Posteriori (MAP) | 
| p_map.numeric | Bayesian p-value based on the density at the Maximum A Posteriori (MAP) | 
| p_pointnull | Bayesian p-value based on the density at the Maximum A Posteriori (MAP) | 
| p_rope | Probability of being in the ROPE | 
| p_rope.brmsfit | Probability of being in the ROPE | 
| p_rope.data.frame | Probability of being in the ROPE | 
| p_rope.numeric | Probability of being in the ROPE | 
| p_significance | Practical Significance (ps) | 
| p_significance.brmsfit | Practical Significance (ps) | 
| p_significance.data.frame | Practical Significance (ps) | 
| p_significance.get_predicted | Practical Significance (ps) | 
| p_significance.numeric | Practical Significance (ps) | 
| p_to_bf | Convert p-values to (pseudo) Bayes Factors | 
| p_to_bf.default | Convert p-values to (pseudo) Bayes Factors | 
| p_to_bf.numeric | Convert p-values to (pseudo) Bayes Factors | 
| p_to_pd | Convert between Probability of Direction (pd) and p-value. | 
| reshape_draws | Reshape estimations with multiple iterations (draws) to long format | 
| reshape_iterations | Reshape estimations with multiple iterations (draws) to long format | 
| rnorm_perfect | Empirical Distributions | 
| rope | Region of Practical Equivalence (ROPE) | 
| rope.brmsfit | Region of Practical Equivalence (ROPE) | 
| rope.data.frame | Region of Practical Equivalence (ROPE) | 
| rope.numeric | Region of Practical Equivalence (ROPE) | 
| rope_range | Find Default Equivalence (ROPE) Region Bounds | 
| rope_range.default | Find Default Equivalence (ROPE) Region Bounds | 
| sensitivity_to_prior | Sensitivity to Prior | 
| sensitivity_to_prior.stanreg | Sensitivity to Prior | 
| sexit | Sequential Effect eXistence and sIgnificance Testing (SEXIT) | 
| sexit_thresholds | Find Effect Size Thresholds | 
| si | Compute Support Intervals | 
| si.data.frame | Compute Support Intervals | 
| si.get_predicted | Compute Support Intervals | 
| si.numeric | Compute Support Intervals | 
| si.stanreg | Compute Support Intervals | 
| simulate_correlation | Data Simulation | 
| simulate_difference | Data Simulation | 
| simulate_prior | Returns Priors of a Model as Empirical Distributions | 
| simulate_prior.brmsfit | Returns Priors of a Model as Empirical Distributions | 
| simulate_simpson | Simpson's paradox dataset simulation | 
| simulate_ttest | Data Simulation | 
| spi | Shortest Probability Interval (SPI) | 
| spi.brmsfit | Shortest Probability Interval (SPI) | 
| spi.data.frame | Shortest Probability Interval (SPI) | 
| spi.get_predicted | Shortest Probability Interval (SPI) | 
| spi.numeric | Shortest Probability Interval (SPI) | 
| update.bayesfactor_models | Bayes Factors (BF) for model comparison | 
| weighted_posteriors | Generate posterior distributions weighted across models | 
| weighted_posteriors.BFBayesFactor | Generate posterior distributions weighted across models | 
| weighted_posteriors.data.frame | Generate posterior distributions weighted across models | 
| weighted_posteriors.stanreg | Generate posterior distributions weighted across models |