bayesplot v1.7.0

bayesplot v1.7.0 is now on CRAN. There are a bunch of new features and fixes in this release. Release notes are available below and also 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=TRUE will be much slower and you can always access 
# the vignettes online at mc-stan.org/bayesplot/articles/

devtools::install_github("stan-dev/bayesplot", ref = "v1.7.0", build_vignettes = FALSE) 

Release notes

• The pars argument of all MCMC plotting functions now supports tidy variable
  selection. See help("tidy-params", package="bayesplot") for details and
  examples. (#161, #183, #188)

• Two new plots have been added for inspecting the distribution of ranks.
  Rank histograms were introduced by the Stan team's new paper on
  MCMC diagnostics. (#178, #179)

  mcmc_rank_hist(): A traditional traceplot (mcmc_trace()) visualizes how
  sampled values the MCMC chains mix over the course of sampling. A rank
  histogram (mcmc_rank_hist()) visualizes how the ranks of values from the
  chains mix together. An ideal plot would show the ranks mixing or overlapping
  in a uniform distribution.

  mcmc_rank_overlay(): Instead of drawing each chain's histogram in a separate
  panel, this plot draws the top edge of the chains' histograms in a single
  panel.

• Added mcmc_trace_data(), which returns the data used for plotting the trace
  plots and rank histograms. (Advances #97)

• ColorBrewer palettes are now available as color schemes via color_scheme_set().
  For example, color_scheme_set("brewer-Spectral") will use the Spectral
  palette. (#177, #190)

• MCMC plots now also accept objects with an as.array method as
  input (e.g., stanfit objects). (#175, #184)

• mcmc_trace() gains an argument iter1 which can be used to label the traceplot starting
  from the first iteration after warmup. (#14, #155, @mcol)

• mcmc_areas() gains an argument area_method which controls how to draw the density
  curves. The default "equal area" constrains the heights so that the curves
  have the same area. As a result, a narrow interval will appear as a spike
  of density, while a wide, uncertain interval is spread thin over the x axis.
  Alternatively "equal height" will set the maximum height on each curve to
  the same value. This works well when the intervals are about the same width.
  Otherwise, that wide, uncertain interval will dominate the visual space
  compared to a narrow, less uncertain interval. A compromise between the two is
  "scaled height" which scales the curves from "equal height" using
  height * sqrt(height). (#163, #169)

• mcmc_areas() correctly plots density curves where the point estimate
  does not include the highest point of the density curve.
  (#168, #169, @jtimonen)

• mcmc_areas_ridges() draws the vertical line at x = 0 over the curves so
  that it is always visible.

• mcmc_intervals() and mcmc_areas() raise a warning if prob_outer is ever
  less than prob. It sorts these two values into the correct order. (#138)

• MCMC parameter names are now always converted to factors prior to
  plotting. We use factors so that the order of parameters in a plot matches
  the order of the parameters in the original MCMC data. This change fixes a
  case where factor-conversion failed. (#162, #165, @wwiecek)

• The examples in ?ppc_loo_pit_overlay() now work as expected. (#166, #167)

• Added "viridisD" as an alternative name for "viridis" to the supported colors.

• Added "viridisE" (the cividis version of viridis) to the supported colors.

• ppc_bars() and ppc_bars_grouped() now allow negative integers as input. (#172, @jeffpollock9)