(GitHub issue/PR numbers in parentheses)
mcmc_parcoord() for parallel coordinates plots of MCMC draws (optionally including HMC/NUTS diagnostic information). (#108)mcmc_scatter gains an np argument for specifying NUTS parameters, which allows highlighting divergences in the plot. (#112)_data don’t make the plots, they just return the data prepared for plotting (more of these to come in future releases):
ppc_intervals_data() (#101)ppc_ribbon_data() (#101)mcmc_parcoord_data() (#108)mcmc_rhat_data() (#110)mcmc_neff_data() (#110)ppc_stat_grouped(), ppc_stat_freqpoly_grouped() gain a facet_args argument for controlling ggplot2 faceting (many of the mcmc_ functions already have this).divergences argument to mcmc_trace() has been deprecated in favor of np (NUTS parameters) to match the other functions that have an np argument.mcmc_rhat() (#105).(GitHub issue/PR numbers in parentheses)
bayesplot::theme_default is now set as the default ggplot2 plotting theme when bayesplot is loaded, which makes changing the default theme using ggplot2::theme_set possible. Thanks to @gavinsimpson. (#87)mcmc_hist and mcmc_hist_by_chain now take a freq argument that defaults to TRUE (behavior is like freq argument to R’s hist function).ts object for y in PPC plots no longer results in an error. Thanks to @helske. (#94)mcmc_intervals doesn’t use round lineends anymore as they slightly exaggerate the width of the intervals. Thanks to @tjmahr. (#96)(GitHub issue/PR numbers in parentheses)
A lot of new stuff in this release:
divergences is specified in call to mcmc_trace but there are not actually any divergent transitions.merge_chains argument to mcmc_nuts_energy now defaults to FALSE.mcmc_* functions, transformations are recycled if transformations argument is specified as a single function rather than a named list. Thanks to @tklebel. (#64)ppc_violin_grouped there is now the option of showing y as a violin, points, or both. Thanks to @silberzwiebel. (#74)color_scheme_get now has an optional argument i for selecting only a subset of the colors.mcmc_pairs, which is essentially a ggplot2+grid implementation of rstan’s pairs.stanfit method. (#67)mcmc_hex, which is similar to mcmc_scatter but using geom_hex instead of geom_point. This can be used to avoid overplotting. (#67)overlay_function convenience function. Example usage: add a Gaussian (or any distribution) density curve to a plot made with mcmc_hist.mcmc_recover_scatter and mcmc_recover_hist, which are similar to mcmc_recover_intervals and compare estimates to “true” values used to simulate data. (#81, #83)ppc_rootogram for use with models for count data. Thanks to @paul-buerkner. (#28)ppc_bars, ppc_bars_grouped for use with models for ordinal, categorical and multinomial data. Thanks to @silberzwiebel. (#73)ppc_loo_pit for assessing the calibration of marginal predictions. (#72)ppc_loo_intervals, ppc_loo_ribbon for plotting intervals of the LOO predictive distribution. (#72) (GitHub issue/PR numbers in parentheses)
png device. Thanks to TJ Mahr. (#51)xaxis_title(FALSE) and yaxis_title(FALSE) now set axis titles to NULL rather than changing theme elements to element_blank(). This makes it easier to add axis titles to plots that don’t have them by default. Thanks to Bill Harris. (#53)divergences to mcmc_trace function. For models fit using HMC/NUTS this can be used to display divergences as a rug at the bottom of the trace plot. (#42)stat argument for all ppc_stat_* functions now accepts a function instead of only the name of a function. (#31)ppc_error_hist_grouped for plotting predictive errors by level of a grouping variable. (#40)mcmc_recover_intervals for comparing MCMC estimates to “true” parameter values used to simulate the data. (#56)bayesplot_grid for juxtaposing plots and enforcing shared axis limits. (#59)