tidybayes 3.0.6

Deprecations:

• Using recover_types() with character vectors is now deprecated, as the expected order of the levels of the variable is ambiguous, leading to hard-to-spot errors where indices and names do not line up correctly. A warning message has been added suggesting the use of factors instead. (#312)

Bug fixes:

• Make test for residual_draws() more robust (due to change in default value of the method argument to brms::residuals()). (#313)

tidybayes 3.0.4

Bug fixes:

• Fix test for summarise_draws.grouped_df() due to changes in how posterior::summarise_draws() handles the num_args argument.

tidybayes 3.0.3

• Much improved documentation of the difference between epred, linpred, and predicted in add_XXX_rvars().
• Fixes for compatibility with changes and deprecations in dplyr and tidyr.
• Add note about argument name changes to fitted_draws() deprecation warning (#288).
• More correct error message for epred_draws() on rethinking models.
• Use posterior::as_draws_df() instead of As.mcmc.list() for rstan objects.

tidybayes 3.0.2

• Future-proof against changes coming in ggdist 3.1 (unification of stat_... and stat_dist_...)
• Minor fixes for R 3.5/3.6 compatibility
• Use @examplesIf for conditional examples (#294)
• Warn when input names in compose_data() are overwritten by generated names (#295)

tidybayes 3.0.1

Minor update for compatibility with brms 2.16.0.

tidybayes 3.0.0

Breaking changes and deprecations:

• The [add_]XXX_draws() (predicted_draws(), add_predicted_draws(), etc) functions have been substantially restructured:
  • add_fitted_draws() and fitted_draws() are now deprecated, along with the scale argument. Several years’ teaching experience has demonstrated that “fitted” is a very confusing name for students. Use the more-specific [add_]linpred_draws() if you want draws from the linear predictor or the new [add_]epred_draws() if you want draws from the expectation of the posterior predictive (which is what fitted_draws() was most typically used for).
  • Arguments for renaming output columns are now all called value, but retain function-specific default column names. E.g. the prediction argument for predicted_draws() is now spelled value but has a default of ".prediction". One breaking change is that the default output column for linpred_draws() is now ".linpred" instead of ".value". This should make it easier to combine outputs across multiple functions while also making it easier to remember the name of the argument that changes the output column name.
  • The n argument is now spelled ndraws to be more consistent with terminology in the posterior package and to prevent partial argument name matching bugs with newdata.
  • The first argument to all of these functions is now object instead of model, in order to match with argument names in posterior_predict(), etc. This was necessary to prevent partial argument name matching bugs with certain model types in rstanarm that have an m argument to their prediction functions.

New features:

• Support for the new posterior package:
  • Several new _rvars counterparts to the _draws family of functions, including spread_rvars(), gather_rvars(), epred_rvars(), linpred_rvars(), and predicted_rvars(), which add columns of posterior::rvar() objects to data frames instead of long-format columns of draws. These can be easier to read and take up less memory than the long-format data frames of draws. See vignette("tidy-posterior") for examples.
  • The nest_rvars() and unnest_rvars() functions for converting between data frames of rvars and long format data frames of draws.
  • tidy_draws() has been rebuilt on top of posterior::as_draws_df(), which means it should support even more model types and benefit from efficiency improvements in posterior. This means that cmdstanr is now supported, for example.
  • An implementation of posterior::summarise_draws() for grouped data frames of draws: summarise_draws.grouped_df()
  • compare_levels() now supports data frames of posterior::rvar()s.
• The epred_draws(), linpred_draws(), and predicted_draws() functions should now support any models that implement posterior_epred(), posterior_linpred(), and posterior_predict() so long as they take a newdata argument.
• Several dependencies have been removed or demoted to Suggests, including forcats, plyr, and purrr.
• Added the option to set the seed when subsampling to several functions (#276).

New documentation:

• vignette("tidy-posterior") describing the use of tidybayes with posterior, and particularly the posterior::rvar() data type. This vignette also includes an updated version of the ordinal regression example from vignette("tidy-brms"), now with an illustration of the relationship between the latent linear predictor and the category-level probabilities.

Bug fixes:

• Omit sampler parameters in tidy_draws() if retrieving them results in an error (#244)

tidybayes 2.3.1

• Allow user-specified names for comparisons in compare_levels() (#272)
• Allow contrasts from the emmeans package to be used with compare_levels() via the new emmeans_comparison() function (#272)

tidybayes 2.1.1

• Initial split of tidybayes into tidybayes and ggdist (#250). ggdist now contains all stats/geoms from tidybayes (except deprecated ones), all support functions for stats/geoms (such as point_interval()), vignette("slabinterval"), and vignette("freq-uncertainty-vis"). tidybayes retains all other functions, and re-exports all ggdist functions for now.
• All stats and geoms now support automatic orientation detection (#257), following the new automatic orientation detection approach in ggplot2. If needed, this can be overridden using the existing orientation parameter.
  • All h-suffix geoms are now deprecated. The h-suffix geoms have been left in tidybayes and give a deprecation warning when used; they cannot be used from ggdist directly.
  • The h-suffix point_interval() functions are also deprecated, since they are not needed in tidybayes nor in ggplot2::stat_summary().
  • geom_interval(), geom_pointinterval(), and geom_lineribbon() no longer automatically set the ymin and ymax aesthetics if .lower or .upper are present in the data. This allows them to work better with automatic orientation detection (and was a bad feature to have existed in the first place anyway). The deprecated tidybayes::geom_intervalh() and tidybayes::geom_pointintervalh() still automatically set those aesthetics, since they are deprecated anyway (so supporting the old behavior is fine in these functions).
• geom_lineribbon()/stat_lineribbon() now supports a step argument for creating stepped lineribbons (#249). H/T to Solomon Kurz for the suggestion.
• ggdist now has its own implementation of the scaled and shifted Student’s t distribution (dstudent_t(), qstudent_t(), etc), since it is very useful for visualizing confidence distributions. That is re-exported in tidybayes as well`.
• All deprecated functions and geoms now throw deprecation warnings (previously, several deprecated functions did not).

tidybayes 2.0.3

Minor fixes for changes in tibble 3.0.0

tidybayes 2.0.2

Various minor forward and backward compatibility fixes:

• Fix stringsAsFactors issues for R 4
• Fix issues with [[<- for R 4
• Fix minor issues with dplyr 1.0.0
• Use parse() instead of str2lang() for compatibility with R <= 3.6

tidybayes 2.0.1

• Various geoms and stats have been merged together under the geom_slabinterval() and stat_slabinterval() “meta-geom” (#84). This has enabled a bunch of new geoms to be created (see vignette("slabinterval") and fixed a number of outstanding issues:
  • Histogram geoms and histogram+interval geoms (#162)
  • CCDF bar charts and gradient plots
  • The alpha aesthetic can now be mapped on eye plots (and all related geoms) (#163)
  • Vertical version of eye plot (and vertical/horizontal variants of all slabinterval variants) (#56)
  • Intervals and densities are now correctly grouped in eye plots (e.g. when dodging) (#83)
  • Fill and color aesthetics can now be mapped within the slab part of eyes (and all slabintervals), allowing gradients to be made easily (#136) and regions of practical equivalence (ROPEs) to be annotated easily. Examples of ROPEs have been added to the main vignettes (#129).
  • Intervals and eyes support position = "dodge" correctly (#180)
  • The new geoms (and replacements for old ones) have custom scales allowing fine-grained targeting of fill, color, and size aesthetics of all the component parts of the composite geoms.
  • There is a new sub-family of auto-sizing Wilkinson dotplot stats and geoms, geom_dots() and geom_dotsinterval() (#210). These include a quantiles parameter on the stats to make it easy to create quantile dotplots.
• Analytical distributions can be visualized using the new stat_dist_... family of geoms for both the geom_slabinterval() family and geom_lineribbon() (see stat_dist_slabinterval() and stat_dist_lineribbon()).
• The new parse_dist(), which parses distribution specifications (like normal(0,1)) into tidy columns, can be combined with the stat_dist_... family of geoms to easily to visualize priors (e.g. from brms).
• New distribution functions for the marginal LKJ distribution (dlkjcorr_marginal() and company), combined with parse_dist() and the stat_dist_... family make it easy to visualize the marginal LKJ prior on a cell in a correlation matrix. (#191 #192)
• There is a new vignette on frequentist uncertainty visualization, vignette("freq-uncertainty-vis"), also made possible by the new stat_dist_... family of geoms (#188)
• tidy_draws() can now be applied to already-tidied data frames, allowing dependent functions (like spread_draws() and gather_draws()) to also be applied to data frames directly (#82). This can be a useful optimization in workflows where the initial tidying is slow but spreading/gathering is fast (see discussion in #144)
• Kruschke-style distribution-of-distribution plots are now easier to construct with stat_dist_slabh(). An example of this usage is in vignette("tidy-brms").
• hdi() now uses trimmed densities by default to avoid odd behavior with bounded distributions (#165).
• compare_levels(comparison = ) now uses a modern tidy approach to dealing with unevaluated expressions, so rlang::exprs() can be used in place of plyr::.() (#174, #175)
• geom_lineribbon() now works with ggnewscale (#178)
• fitted_draws()/predicted_draws() give more helpful error messages on unsupported models (#177)

tidybayes 1.1.0

New features and documentation:

• Support matrices, n-d arrays, and lists of vectors in compose_data (#159)
• Support nested vectors, matrices, n-d arrays, and ragged arrays through x[.,.] syntax in gather/spread_draws (#154)
• Add detached-line-ribbon HOPs example for ordinal models in brms vignette

Bug fixes:

• Fixed errors on CRAN from changes in brms
• Properly handle Dirichlet responses in predicted_draws (#164)

tidybayes 1.0.4

New features and documentation:

• Initial support for add_residual_draws, towards #133
• Add tidybayes-residuals vignette
• Add add_draws to support models that add_[fitted|predicted]_draws does not (closes #149)
• Add sample_draws to make it easier to take fewer draws anywhere in the pipeline (towards #144)
• Add hypothetical outcome plots (HOPs) to examples

Minor changes:

• Fixed errors on CRAN from changes in dplyr
• Fix bug to support multivariate models in predicted_draws(), closes #134
• Add support for emm_list in gather_emmeans_draws(), closes #126
• Default for show.legend no longer omits all guides
• Make default geom_lineribbon() color black, closes #153

tidybayes 1.0.3

• Added gather_pairs method for creating custom scatterplot matrices (and more!)
• Ordinal models in brms now use original category labels (#122)
• NA values are now better supported in point_interval, and it has an na.rm argument (#123)
• Added sampler diagnostics to tidy_draws() Stan output (#109)
• Added MCMCglmm+emmeans example to vignettes
• Add guards to prevent usage of packages listed in Suggests

tidybayes 1.0.0

Major changes:

• First CRAN release.
• Various function, argument, and column name changes towards unification with the Stan ecosystem. See help(“tidybayes-deprecated”) for more information.