bayesplot v1.2.0 released

[Jonah Gabry]

Dear Stan+R users,

Version 1.2.0 of the bayesplot R package is now available on CRAN. It has a few fixes and also a lot of new features.

The release notes are copied below and the package can be installed in the standard way from CRAN using

install.packages("bayesplot")

 - Stan Development Team

----- 

bayesplot 1.2.0 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 the 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)