ggdist 3.3.0

Breaking changes: The following changes, mostly due to new default density
estimators, may cause some plots on sample data to change. Changes should usually
be small, and generally should result in more accurate density estimation. Revert
to the old behavior by setting density = density_unbounded(bandwidth = "nrd0").

• stat_slabinterval() now uses density_bounded() as its default density
  estimator, which uses a bounded density estimator that also estimates the
  bounds of the data. The default bandwidth estimator is also now bandwidth_dpi(),
  which is the Sheather-Jones direct plug-in estimator (the same as
  stats::bw.SJ(..., method = "dpi")). These changes may cause existing charts
  using densities to change; usually only slightly. These changes should be worth
  it, as they should drastically improve the accuracy of density estimates,
  especially on bounded data, and should have little noticeable impact on densities
  on unbounded data.
• density_bounded() now estimates bounds from the data when not provided
  (i.e. when one of bounds is NA). See the bounder_ functions (e.g.
  bounder_cdf(), bounder_cooke()) for more on bounds estimation.
• Improved Mode() and hdi() estimators based on bounded density estimator.

New features and enhancements:

• Improved hdci() estimator using quantile estimation.
• Histograms are now implemented using density_histogram(), a histogram
  density estimator. Finer-grained control of bin positions is now possible
  using the breaks argument (including the new breaks_fixed() for manually-specified
  bin widths) and the align argument (including the new align_boundary() and
  align_center() for choosing how to align bin positions to reference points). (#118)
• New geom_spike() and stat_spike() for adding spike annotations to slabs
  created with geom_slabinterval() or stat_slabinterval(). See example
  in vignette("slabinterval"). (#58, #124)
• parse_dist() now outputs distributional objects in a .dist_obj column in
  addition to the name-plus-arguments (.dist+.args) format, and these objects respect truncation
  parameters from prior specifications. This makes it easier to visualize standard
  deviation priors, for example, giving a better solution to #20.
• marginalize_lkjcorr() adjusts the .dist_obj column output by parse_dist()
  in addition to the .dist and .args columns.
• geom_lineribbon() now obeys the order aesthetic, allowing you to arbitrarily
  set the draw order of ribbons (#171). Enabled by this change, stat_lineribbon()
  now sets order = after_stat(level) by default, making its draw order more correct
  by ensuring all ribbons of the same level are drawn together.
• Some improved error messages using cli.
• Very experimental adaptive KDE is available through the adapt parameter;
  note that it is unsupported and both the implementation and interface are
  highly likely to change.

Deprecations:

• The slab_type parameter for stat_slabinterval() is now deprecated in favor
  of mapping the corresponding computed variable (pdf or cdf) onto the desired
  aesthetic. For slab_type = "histogram", use the pdf computed variable
  combined with the new density_histogram() density estimator (e.g. set
  density = "histogram"). (#165)

Bug fixes:

• Ensure scale transformations work even when no slab is present; e.g. in
  stat_interval(). (#168)
• Ensure curve_interval() works with posterior::rvars. (#158)
• geom_lineribbon() draw order is now correct even when some portions of a
  ribbon has NA widths. (#171)
• Improve the appearance of logical fill conditions at bin edges on histograms. (#175)