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)

bayesplot v1.6.0

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)

bayesplot v1.5.0

bayesplot v1.5.0 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 = "v1.5.0", build_vignettes = TRUE) 

Release notes

(GitHub issue/PR numbers in parentheses)

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

• Two new plots that visualize posterior density using ridgelines (ggridges pkg). 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 corresponding _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 v1.4.0

bayesplot v1.4.0 is now on CRAN.

Until CRAN binaries are built (usually a few days) you can install the update using

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

or by installing from GitHub using

devtools::install_github("stan-dev/bayesplot", ref = "v1.4.0", build_vignettes = TRUE)

Release notes

(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 v1.3.0

Release notes

(GitHub issue/PR numbers in parentheses)

The biggest change is:

• bayesplot::theme_default is now set as the default ggplot2 plotting theme
  when bayesplot is loaded instead of hardcoded for each plot. This makes
  changing the default theme using ggplot2::theme_set possible.
  Thanks to @gavinsimpson. (#87)

Some minor changes:

• 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 v1.2.0

bayesplot v1.2.0 is now on CRAN and can be installed with install.packages("bayesplot").
There is a lot of new stuff in this release!

Release notes

(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 v1.1.0

bayesplot v1.1.0 is now on CRAN and can be installed using install.packages("bayesplot").

Release notes

(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

• Introduce ppc_error_hist_grouped for plotting predictive errors
  by level of a grouping variable. (#40)
• Introduce mcmc_recover_intervals for comparing MCMC estimates to "true"
  parameter values used to simulate the data. (#56)
• 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)
• Introduce bayesplot_grid function for juxtaposing plots and enforcing shared
  axis limits. (#59)
• The stat argument for all ppc_stat_* functions now accepts a function
  instead of only the name of a function. (#31)

Initial CRAN release

Update README.md

[ci skip]

Beta Release

To install this beta release of the bayespot package you will also need the preview version of the
upcoming ggplot2 update (not the CRAN version):

if (!require("devtools"))
  install.packages("devtools")

devtools::install_github("hadley/ggplot2")
devtools::install_github("stan-dev/bayesplot", build_vignettes = TRUE)