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bayesplot v1.2.0

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@jgabry jgabry released this 14 Apr 20:18
· 1124 commits to master since this release
v1.2.0
50f249e

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)
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