Package: bayesplot 1.11.1.9000

Jonah Gabry

bayesplot: Plotting for Bayesian Models

Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.

Authors:Jonah Gabry [aut, cre], Tristan Mahr [aut], Paul-Christian Bürkner [ctb], Martin Modrák [ctb], Malcolm Barrett [ctb], Frank Weber [ctb], Eduardo Coronado Sroka [ctb], Teemu Sailynoja [ctb], Aki Vehtari [ctb]

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NEWS

# Install 'bayesplot' in R:
install.packages('bayesplot', repos = c('https://stan-dev.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/stan-dev/bayesplot/issues

On CRAN:

bayesianggplot2mcmcpandocstanstatistical-graphicsvisualization

16.81 score 432 stars 93 packages 6.5k scripts 26k downloads 16 mentions 162 exports 45 dependencies

Last updated 3 months agofrom:ce4f5d1a4f. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winNOTEOct 31 2024
R-4.5-linuxNOTEOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:abline_01available_mcmcavailable_ppcavailable_ppdbayesplot_gridbayesplot_theme_getbayesplot_theme_replacebayesplot_theme_setbayesplot_theme_updatecolor_scheme_getcolor_scheme_setcolor_scheme_viewexample_group_dataexample_mcmc_drawsexample_x_dataexample_y_dataexample_yrep_drawsfacet_bgfacet_textgrid_lineshline_0hline_atlbublegend_movelegend_nonelegend_textlog_posteriormcmc_acfmcmc_acf_barmcmc_areasmcmc_areas_datamcmc_areas_ridgesmcmc_areas_ridges_datamcmc_combomcmc_densmcmc_dens_chainsmcmc_dens_chains_datamcmc_dens_overlaymcmc_hexmcmc_histmcmc_hist_by_chainmcmc_intervalsmcmc_intervals_datamcmc_neffmcmc_neff_datamcmc_neff_histmcmc_nuts_acceptancemcmc_nuts_divergencemcmc_nuts_energymcmc_nuts_stepsizemcmc_nuts_treedepthmcmc_pairsmcmc_parcoordmcmc_parcoord_datamcmc_rank_ecdfmcmc_rank_histmcmc_rank_overlaymcmc_recover_histmcmc_recover_intervalsmcmc_recover_scattermcmc_rhatmcmc_rhat_datamcmc_rhat_histmcmc_scattermcmc_tracemcmc_trace_datamcmc_trace_highlightmcmc_violinneff_rationuts_paramsoverlay_functionpairs_conditionpairs_style_nppanel_bgparam_glueparam_rangeparcoord_style_npplot_bgpp_checkppc_barsppc_bars_datappc_bars_groupedppc_boxplotppc_datappc_densppc_dens_overlayppc_dens_overlay_groupedppc_ecdf_overlayppc_ecdf_overlay_groupedppc_error_binnedppc_error_datappc_error_histppc_error_hist_groupedppc_error_scatterppc_error_scatter_avgppc_error_scatter_avg_groupedppc_error_scatter_avg_vs_xppc_freqpolyppc_freqpoly_groupedppc_histppc_intervalsppc_intervals_datappc_intervals_groupedppc_km_overlayppc_km_overlay_groupedppc_loo_intervalsppc_loo_pitppc_loo_pit_datappc_loo_pit_overlayppc_loo_pit_qqppc_loo_ribbonppc_pit_ecdfppc_pit_ecdf_groupedppc_ribbonppc_ribbon_datappc_ribbon_groupedppc_rootogramppc_scatterppc_scatter_avgppc_scatter_avg_datappc_scatter_avg_groupedppc_scatter_datappc_statppc_stat_2dppc_stat_datappc_stat_freqpolyppc_stat_freqpoly_groupedppc_stat_groupedppc_violin_groupedppd_boxplotppd_datappd_densppd_dens_overlayppd_ecdf_overlayppd_freqpolyppd_freqpoly_groupedppd_histppd_intervalsppd_intervals_datappd_intervals_groupedppd_ribbonppd_ribbon_datappd_ribbon_groupedppd_statppd_stat_2dppd_stat_datappd_stat_freqpolyppd_stat_freqpoly_groupedppd_stat_groupedrhatscatter_style_nptheme_defaulttrace_style_npvarsvline_0vline_atxaxis_textxaxis_ticksxaxis_titleyaxis_textyaxis_ticksyaxis_title

Dependencies:abindbackportscheckmateclicolorspacedistributionaldplyrfansifarvergenericsggplot2ggridgesgluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgconfigplyrposteriorR6RColorBrewerRcppreshape2rlangscalesstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr

Graphical posterior predictive checks using the bayesplot package

Jonah Gabry

Rendered fromgraphical-ppcs.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2021-01-07
Started: 2017-08-01

Plotting MCMC draws using the bayesplot package

Jonah Gabry

Rendered fromplotting-mcmc-draws.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2022-11-15
Started: 2017-08-01

Visual MCMC diagnostics using the bayesplot package

Jonah Gabry and Martin Modrák

Rendered fromvisual-mcmc-diagnostics.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2024-01-30
Started: 2017-08-01

Readme and manuals

Help Manual

Help pageTopics
*bayesplot*: Plotting for Bayesian Modelsbayesplot-package bayesplot
Get or view the names of available plotting or data functionsavailable_mcmc available_ppc available_ppd
Arrange plots in a gridbayesplot_grid
Get, set, and modify the active *bayesplot* themebayesplot_theme_get bayesplot_theme_replace bayesplot_theme_set bayesplot_theme_update
Set, get, or view *bayesplot* color schemesbayesplot-colors color_scheme_get color_scheme_set color_scheme_view
Extract quantities needed for plotting from model objectsbayesplot-extractors log_posterior log_posterior.CmdStanMCMC log_posterior.stanfit log_posterior.stanreg neff_ratio neff_ratio.CmdStanMCMC neff_ratio.stanfit neff_ratio.stanreg nuts_params nuts_params.CmdStanMCMC nuts_params.list nuts_params.stanfit nuts_params.stanreg rhat rhat.CmdStanMCMC rhat.stanfit rhat.stanreg
Convenience functions for adding or changing plot detailsabline_01 bayesplot-helpers facet_bg facet_text grid_lines hline_0 hline_at lbub legend_move legend_none legend_text overlay_function panel_bg plot_bg vline_0 vline_at xaxis_text xaxis_ticks xaxis_title yaxis_text yaxis_ticks yaxis_title
Combination plotsMCMC-combos mcmc_combo
General MCMC diagnosticsMCMC-diagnostics mcmc_acf mcmc_acf_bar mcmc_neff mcmc_neff_data mcmc_neff_hist mcmc_rhat mcmc_rhat_data mcmc_rhat_hist
Histograms and kernel density plots of MCMC drawsMCMC-distributions mcmc_dens mcmc_dens_chains mcmc_dens_chains_data mcmc_dens_overlay mcmc_hist mcmc_hist_by_chain mcmc_violin
Plot interval estimates from MCMC drawsMCMC-intervals mcmc_areas mcmc_areas_data mcmc_areas_ridges mcmc_areas_ridges_data mcmc_intervals mcmc_intervals_data
Diagnostic plots for the No-U-Turn-Sampler (NUTS)MCMC-nuts mcmc_nuts_acceptance mcmc_nuts_divergence mcmc_nuts_energy mcmc_nuts_stepsize mcmc_nuts_treedepth NUTS
Plots for Markov chain Monte Carlo simulationsMCMC MCMC-overview
Parallel coordinates plot of MCMC drawsMCMC-parcoord mcmc_parcoord mcmc_parcoord_data parcoord_style_np
Compare MCMC estimates to "true" parameter valuesMCMC-recover mcmc_recover_hist mcmc_recover_intervals mcmc_recover_scatter
Scatterplots of MCMC drawsMCMC-scatterplots mcmc_hex mcmc_pairs mcmc_scatter pairs_condition pairs_style_np scatter_style_np
Trace and rank plots of MCMC drawsMCMC-traces mcmc_rank_ecdf mcmc_rank_hist mcmc_rank_overlay mcmc_trace mcmc_trace_data mcmc_trace_highlight trace_style_np
Posterior (or prior) predictive checks (S3 generic and default method)pp_check pp_check.default
PPC censoringPPC-censoring ppc_km_overlay ppc_km_overlay_grouped
PPCs for discrete outcomesPPC-discrete ppc_bars ppc_bars_data ppc_bars_grouped ppc_rootogram
PPC distributionsPPC-distributions ppc_boxplot ppc_data ppc_dens ppc_dens_overlay ppc_dens_overlay_grouped ppc_ecdf_overlay ppc_ecdf_overlay_grouped ppc_freqpoly ppc_freqpoly_grouped ppc_hist ppc_pit_ecdf ppc_pit_ecdf_grouped ppc_violin_grouped
PPC errorsPPC-errors ppc_error_binned ppc_error_data ppc_error_hist ppc_error_hist_grouped ppc_error_scatter ppc_error_scatter_avg ppc_error_scatter_avg_grouped ppc_error_scatter_avg_vs_x
PPC intervalsPPC-intervals ppc_intervals ppc_intervals_data ppc_intervals_grouped ppc_ribbon ppc_ribbon_data ppc_ribbon_grouped
LOO predictive checksPPC-loo ppc_loo_intervals ppc_loo_pit ppc_loo_pit_data ppc_loo_pit_overlay ppc_loo_pit_qq ppc_loo_ribbon
Graphical posterior predictive checkingPPC PPC-overview
PPC scatterplotsPPC-scatterplots ppc_scatter ppc_scatter_avg ppc_scatter_avg_data ppc_scatter_avg_grouped ppc_scatter_data
PPC test statisticsPPC-statistics PPC-test-statistics ppc_stat ppc_stat_2d ppc_stat_data ppc_stat_freqpoly ppc_stat_freqpoly_grouped ppc_stat_grouped
PPD distributionsPPD-distributions ppd_boxplot ppd_data ppd_dens ppd_dens_overlay ppd_ecdf_overlay ppd_freqpoly ppd_freqpoly_grouped ppd_hist
PPD intervalsPPD-intervals ppd_intervals ppd_intervals_data ppd_intervals_grouped ppd_ribbon ppd_ribbon_data ppd_ribbon_grouped
Plots of posterior or prior predictive distributionsPPD PPD-overview
PPD test statisticsPPD-statistics PPD-test-statistics ppd_stat ppd_stat_2d ppd_stat_data ppd_stat_freqpoly ppd_stat_freqpoly_grouped ppd_stat_grouped
Default *bayesplot* plotting themetheme_default
Tidy parameter selectionparam_glue param_range tidy-params