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bayestestR  

Understand and Describe Bayesian Models and Posterior Distributions
View on CRAN: Click here


Download and install bayestestR package within the R console
Install from CRAN:
install.packages("bayestestR")

Install from Github:
library("remotes")
install_github("cran/bayestestR")

Install by package version:
library("remotes")
install_version("bayestestR", "0.13.2")



Attach the package and use:
library("bayestestR")
Maintained by
Dominique Makowski
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-04-08
Latest Update: 2024-02-12
Description:
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 ) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) .
How to cite:
Dominique Makowski (2019). bayestestR: Understand and Describe Bayesian Models and Posterior Distributions. R package version 0.13.2, https://cran.r-project.org/web/packages/bayestestR
Previous versions and publish date:
0.1.0 (2019-04-08 11:42), 0.2.0 (2019-05-29 17:10), 0.2.2 (2019-06-20 14:50), 0.2.5 (2019-08-06 13:20), 0.3.0 (2019-09-22 18:30), 0.4.0 (2019-10-20 09:20), 0.5.0 (2020-01-18 09:10), 0.5.1 (2020-01-27 07:30), 0.5.2 (2020-02-12 06:10), 0.5.3 (2020-03-26 07:10), 0.6.0 (2020-04-20 08:10), 0.7.0 (2020-06-19 11:00), 0.7.2 (2020-07-20 11:30), 0.7.5 (2020-10-22 16:40), 0.8.0 (2020-12-05 10:30), 0.8.2 (2021-01-26 18:40), 0.9.0 (2021-04-08 07:40), 0.10.0 (2021-05-31 08:40), 0.10.5 (2021-07-26 11:40), 0.11.0 (2021-09-03 02:10), 0.11.5 (2021-10-30 16:00), 0.12.1 (2022-05-02 09:40), 0.13.0 (2022-09-18 04:46), 0.13.1 (2023-04-07 17:20)
Other packages that cited bayestestR R package
View bayestestR citation profile
Other R packages that bayestestR depends, imports, suggests or enhances
Functions, R codes and Examples using the bayestestR R package
Some associated functions: area_under_curve . as.data.frame.density . as.numeric.p_direction . bayesfactor . bayesfactor_inclusion . bayesfactor_models . bayesfactor_parameters . bayesfactor_restricted . bayestestR-package . bci . bic_to_bf . check_prior . ci . contr.equalprior . convert_bayesian_as_frequentist . cwi . density_at . describe_posterior . describe_prior . diagnostic_draws . diagnostic_posterior . disgust . distribution . dot-extract_priors_rstanarm . dot-prior_new_location . dot-select_nums . effective_sample . equivalence_test . estimate_density . eti . hdi . map_estimate . mcse . mediation . model_to_priors . overlap . p_direction . p_map . p_rope . p_significance . p_to_bf . pd_to_p . point_estimate . reexports . reshape_iterations . rope . rope_range . sensitivity_to_prior . sexit . sexit_thresholds . si . simulate_correlation . simulate_prior . simulate_simpson . spi . unupdate . weighted_posteriors . 
Some associated R codes: area_under_curve.R . as.list.R . bayesfactor.R . bayesfactor_inclusion.R . bayesfactor_models.R . bayesfactor_parameters.R . bayesfactor_restricted.R . bayestestR-package.R . bci.R . bic_to_bf.R . check_prior.R . ci.R . contr.equalprior.R . convert_bayesian_to_frequentist.R . convert_pd_to_p.R . cwi.R . datasets.R . describe_posterior.R . describe_prior.R . diagnostic_draws.R . diagnostic_posterior.R . distribution.R . effective_sample.R . equivalence_test.R . estimate_density.R . eti.R . format.R . hdi.R . map_estimate.R . mcse.R . mediation.R . model_to_priors.R . overlap.R . p_direction.R . p_map.R . p_rope.R . p_significance.R . p_to_bf.R . plot.R . point_estimate.R . print.R . print.bayesfactor_models.R . print.equivalence_test.R . print.rope.R . print_html.R . print_md.R . reexports.R . reshape_iterations.R . rope.R . rope_range.R . sensitivity_to_prior.R . sexit.R . sexit_thresholds.R . si.R . simulate_data.R . simulate_priors.R . simulate_simpson.R . spi.R . unupdate.R . utils.R . utils_bayesfactor.R . utils_check_collinearity.R . utils_clean_stan_parameters.R . utils_hdi_ci.R . utils_posterior.R . utils_print_data_frame.R . weighted_posteriors.R . zzz.R .  Full bayestestR package functions and examples
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