Package: bayestestR 0.15.2.5
Dominique Makowski
bayestestR: Understand and Describe Bayesian Models and Posterior Distributions
Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.
Authors:
bayestestR_0.15.2.5.tar.gz
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bayestestR.pdf |bayestestR.html✨
bayestestR/json (API)
NEWS
| # Install 'bayestestR' in R: |
| install.packages('bayestestR', repos = c('https://easystats.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/easystats/bayestestr/issues
Pkgdown site:https://easystats.github.io
- disgust - Moral Disgust Judgment
bayes-factorsbayesfactorbayesianbayesian-frameworkcredible-intervaleasystatshacktoberfesthdimapposterior-distributionsrope
Last updated 7 days agofrom:14db384459. Checks:9 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 21 2025 |
| R-4.5-win | OK | Mar 21 2025 |
| R-4.5-mac | OK | Mar 21 2025 |
| R-4.5-linux | OK | Mar 21 2025 |
| R-4.4-win | OK | Mar 21 2025 |
| R-4.4-mac | OK | Mar 21 2025 |
| R-4.4-linux | OK | Mar 21 2025 |
| R-4.3-win | OK | Mar 21 2025 |
| R-4.3-mac | OK | Mar 21 2025 |
Exports:area_under_curveaucbayesfactorbayesfactor_inclusionbayesfactor_modelsbayesfactor_parametersbayesfactor_pointnullbayesfactor_restrictedbayesfactor_ropebayesian_as_frequentistbcaibcibf_inclusionbf_modelsbf_parametersbf_pointnullbf_restrictedbf_ropebic_to_bfcheck_priorcicontr.bayescontr.equalpriorcontr.equalprior_deviationscontr.equalprior_pairscontr.orthonormconvert_bayesian_as_frequentistconvert_p_to_pdconvert_pd_to_pdensity_atdescribe_posteriordescribe_priordiagnostic_drawsdiagnostic_posteriordistributiondistribution_betadistribution_binomdistribution_binomialdistribution_cauchydistribution_chisqdistribution_chisquareddistribution_customdistribution_gammadistribution_gaussiandistribution_mixture_normaldistribution_nbinomdistribution_normaldistribution_poissondistribution_studentdistribution_student_tdistribution_tdistribution_tweediedistribution_uniformeffective_sampleequivalence_testestimate_densityetihdimap_estimatemcsemediationmodel_to_priorsoverlapp_directionp_mapp_pointnullp_ropep_significancep_to_bfp_to_pdpdpd_to_ppoint_estimateprint_htmlprint_mdreshape_drawsreshape_iterationsrnorm_perfectroperope_rangesensitivity_to_priorsexitsexit_thresholdssisimulate_correlationsimulate_differencesimulate_priorsimulate_simpsonsimulate_ttestspiunupdateweighted_posteriors
Dependencies:datawizardinsight
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Area under the Curve (AUC) | area_under_curve auc |
| Coerce to a Data Frame | as.data.frame.density |
| Convert to Numeric | as.numeric.map_estimate as.numeric.p_direction as.numeric.p_map as.numeric.p_significance |
| Bayes Factors (BF) | bayesfactor |
| Inclusion Bayes Factors for testing predictors across Bayesian models | bayesfactor_inclusion bf_inclusion |
| Bayes Factors (BF) for model comparison | as.matrix.bayesfactor_models bayesfactor_models bayesfactor_models.default bf_models update.bayesfactor_models |
| Bayes Factors (BF) for a Single Parameter | bayesfactor_parameters bayesfactor_parameters.data.frame bayesfactor_parameters.numeric bayesfactor_parameters.stanreg bayesfactor_pointnull bayesfactor_rope bf_parameters bf_pointnull bf_rope |
| Bayes Factors (BF) for Order Restricted Models | as.logical.bayesfactor_restricted bayesfactor_restricted bayesfactor_restricted.blavaan bayesfactor_restricted.brmsfit bayesfactor_restricted.data.frame bayesfactor_restricted.emmGrid bayesfactor_restricted.stanreg bf_restricted |
| Bias Corrected and Accelerated Interval (BCa) | bcai bci bci.brmsfit bci.data.frame bci.get_predicted bci.numeric |
| Convert BIC indices to Bayes Factors via the BIC-approximation method. | bic_to_bf |
| Check if Prior is Informative | check_prior check_prior.brmsfit |
| Confidence/Credible/Compatibility Interval (CI) | ci ci.brmsfit ci.data.frame ci.numeric |
| Contrast Matrices for Equal Marginal Priors in Bayesian Estimation | contr.bayes contr.equalprior contr.equalprior_deviations contr.equalprior_pairs contr.orthonorm |
| Convert (refit) a Bayesian model to frequentist | bayesian_as_frequentist convert_bayesian_as_frequentist |
| Density Probability at a Given Value | density_at |
| Describe Posterior Distributions | describe_posterior describe_posterior.data.frame describe_posterior.numeric describe_posterior.stanreg |
| Describe Priors | describe_prior describe_prior.brmsfit |
| Diagnostic values for each iteration | diagnostic_draws |
| Posteriors Sampling Diagnostic | diagnostic_posterior diagnostic_posterior.default diagnostic_posterior.stanreg |
| Moral Disgust Judgment | disgust |
| Empirical Distributions | distribution distribution_beta distribution_binom distribution_binomial distribution_cauchy distribution_chisq distribution_chisquared distribution_custom distribution_gamma distribution_gaussian distribution_mixture_normal distribution_nbinom distribution_normal distribution_poisson distribution_student distribution_student_t distribution_t distribution_tweedie distribution_uniform rnorm_perfect |
| Effective Sample Size (ESS) | effective_sample effective_sample.brmsfit |
| Test for Practical Equivalence | equivalence_test equivalence_test.brmsfit equivalence_test.data.frame equivalence_test.default |
| Density Estimation | estimate_density estimate_density.brmsfit estimate_density.data.frame |
| Equal-Tailed Interval (ETI) | eti eti.brmsfit eti.data.frame eti.get_predicted eti.numeric |
| Highest Density Interval (HDI) | hdi hdi.brmsfit hdi.data.frame hdi.get_predicted hdi.numeric |
| Maximum A Posteriori probability estimate (MAP) | map_estimate map_estimate.brmsfit map_estimate.data.frame map_estimate.get_predicted map_estimate.numeric |
| Monte-Carlo Standard Error (MCSE) | mcse mcse.stanreg |
| Summary of Bayesian multivariate-response mediation-models | mediation mediation.brmsfit |
| Convert model's posteriors to priors (EXPERIMENTAL) | model_to_priors |
| Overlap Coefficient | overlap |
| Probability of Direction (pd) | pd p_direction p_direction.brmsfit p_direction.data.frame p_direction.get_predicted p_direction.numeric |
| Bayesian p-value based on the density at the Maximum A Posteriori (MAP) | p_map p_map.brmsfit p_map.data.frame p_map.get_predicted p_map.numeric p_pointnull |
| Probability of being in the ROPE | p_rope p_rope.brmsfit p_rope.data.frame p_rope.numeric |
| Practical Significance (ps) | p_significance p_significance.brmsfit p_significance.data.frame p_significance.get_predicted p_significance.numeric |
| Convert p-values to (pseudo) Bayes Factors | p_to_bf p_to_bf.default p_to_bf.numeric |
| Convert between Probability of Direction (pd) and p-value. | convert_pd_to_p convert_p_to_pd pd_to_p pd_to_p.numeric p_to_pd |
| Point-estimates of posterior distributions | point_estimate point_estimate.brmsfit point_estimate.data.frame point_estimate.get_predicted point_estimate.numeric |
| Reshape estimations with multiple iterations (draws) to long format | reshape_draws reshape_iterations |
| Region of Practical Equivalence (ROPE) | rope rope.brmsfit rope.data.frame rope.numeric |
| Find Default Equivalence (ROPE) Region Bounds | rope_range rope_range.default |
| Sensitivity to Prior | sensitivity_to_prior sensitivity_to_prior.stanreg |
| Sequential Effect eXistence and sIgnificance Testing (SEXIT) | sexit |
| Find Effect Size Thresholds | sexit_thresholds |
| Compute Support Intervals | si si.data.frame si.get_predicted si.numeric si.stanreg |
| Data Simulation | simulate_correlation simulate_difference simulate_ttest |
| Returns Priors of a Model as Empirical Distributions | simulate_prior simulate_prior.brmsfit |
| Simpson's paradox dataset simulation | simulate_simpson |
| Shortest Probability Interval (SPI) | spi spi.brmsfit spi.data.frame spi.get_predicted spi.numeric |
| Generate posterior distributions weighted across models | weighted_posteriors weighted_posteriors.BFBayesFactor weighted_posteriors.data.frame weighted_posteriors.stanreg |
