bayesplot 1.5.0 2018-03-30

(GitHub issue/PR numbers in parentheses)

• New package documentation website: http://mc-stan.org/bayesplot/

• Two new plots that visualize posterior density using ridgelines. These work well when parameters have similar values and similar densities, as in hierarchical models. (#104)
• mcmc_dens_chains() draws the kernel density of each sampling chain.
• mcmc_areas_ridges() draws the kernel density combined across chains.
• Both functions have a _data() function to return the data plotted by each function.
• mcmc_intervals() and mcmc_areas() have been rewritten. (#103)
• They now use a discrete y-axis. Previously, they used a continuous scale with numeric breaks relabelled with parameter names; this design
caused some unexpected behavior when customizing these plots.
• mcmc_areas() now uses geoms from the ggridges package to draw density curves.
• Added mcmc_intervals_data() and mcmc_areas_data() that return data plotted by mcmc_intervals() and mcmc_areas(). Similarly, ppc_data() returns data plotted ppc_hist() and other ppc plot. (Advances #97)

• Added ppc_loo_pit_overlay() function for a better LOO PIT predictive check. (#123)

• Started using vdiffr to add visual unit tests to the existing PPC unit tests. (#137)

bayesplot 1.4.0 2017-09-12

(GitHub issue/PR numbers in parentheses)

• New plotting function 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)

• New functions with names ending with suffix _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).

• The divergences argument to mcmc_trace() has been deprecated in favor of np (NUTS parameters) to match the other functions that have an np argument.

• Fixed an issue where duplicated rhat values would break mcmc_rhat() (#105).

bayesplot 1.3.0 2017-08-07

(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).

• Using a 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)

bayesplot 1.2.0 2017-04-12

A lot of new stuff in this release. (GitHub issue/PR numbers in parentheses)

Fixes

• Avoid error in some cases when divergences is specified in call to mcmc_trace() but there are not actually any divergent transitions.

• The merge_chains argument to mcmc_nuts_energy() now defaults to FALSE.

New features in existing functions

• For mcmc_*() functions, transformations are recycled if transformations argument is specified as a single function rather than a named list. Thanks to @tklebel. (#64)

• For 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.

• New color schemes: darkgray, orange, viridis, viridisA, viridisB, viridisC. The viridis schemes are better than the other schemes for trace plots (the colors are very distinct from each other).

New functions

• 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)

• New PPC category Discrete with functions:
• 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)
• New PPC category LOO (thanks to suggestions from @avehtari) with functions:
• 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)

bayesplot 1.1.0 2016-12-20

(GitHub issue/PR numbers in parentheses)

Fixes

• Images in vignettes should now render properly using 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)

New features in existing functions

• Add argument 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)

• The stat argument for all ppc_stat_*() functions now accepts a function instead of only the name of a function. (#31)

New functions

• 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)

bayesplot 1.0.0 2016-11-18

Initial CRAN release