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bayesplot v1.6.0
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@jgabry jgabry released this 02 Aug 18:09
· 702 commits to master since this release
v1.6.0
faaee8d

bayesplot v1.6.0 is now on CRAN.
See release notes below or at mc-stan.org/bayesplot/news.

Installation

After CRAN binaries are built (usually a few days) just use install.packages("bayesplot"). Before binaries are available the update can be installed from CRAN using

install.packages("bayesplot", type = "source", repos = "https://cran.rstudio.com/")

or from GitHub using

# note: setting build_vignettes=FALSE will be much faster and you can always access 
# the vignettes at mc-stan.org/bayesplot/articles/

devtools::install_github("stan-dev/bayesplot", ref = "v1.6.0", build_vignettes = TRUE)

Release notes

(GitHub issue/PR numbers in parentheses)

  • Loading bayesplot no longer overrides the ggplot theme! Rather, it sets a theme
    specific for bayesplot. Some packages using bayesplot may still override the
    default ggplot theme (e.g., rstanarm does but only until next release),
    but simply loading bayesplot itself will not. There are new functions for controlling
    the ggplot theme for bayesplot that work like their ggplot2 counterparts but
    only affect plots made using bayesplot. Thanks to Malcolm Barrett. (#117, #149).

    • bayesplot_theme_set()
    • bayesplot_theme_get()
    • bayesplot_theme_update()
    • bayesplot_theme_replace()

  • The Visual MCMC Diagnostics vignette
    has been reorganized and has a lot of useful new content thanks to Martin Modrák. (#144, #153)

  • The LOO predictive checks
    now require loo version >= 2.0.0. (#139)

  • Histogram plots gain a breaks argument that can be used as an alternative to binwidth. (#148)

  • mcmc_pairs()
    now has an argument grid_args to provide a way of passing optional arguments to
    gridExtra::arrangeGrob(). This can be used to add a title to the plot, for example. (#143)

  • ppc_ecdf_overlay()
    gains an argument discrete, which is FALSE by default, but can be used to make the
    Geom more appropriate for discrete data. (#145)

  • PPC intervals plots
    and LOO predictive checks
    now draw both an outer and an inner probability interval, which can be
    controlled through the new argument prob_outer and the already existing
    prob. This is consistent with what is produced by mcmc_intervals().
    (#152, #154, @mcol)

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