modelbased 0.10.0

Breaking Changes

• The deprecated function visualisation_matrix() has been removed. Use
  insight::get_datagrid() instead.

• The "average" option for argument estimate was renamed into "typical".
  The former "average" option is still available, but now returns marginal
  means fully averaged across the sample.

Changes

• The transform argument now also works for estimate_slopes() and for
  estimate_contrasts() with numeric focal terms.

• estimate_contrasts() no longer calls estimate_slopes() for numeric focal
  terms when these are integers with only few values. In this case, it is assumed
  that contrasts of values ("levels") are desired, because integer variables with
  only two to five unique values are factor-alike.

• estimate_contrasts: now supports optional standardized effect sizes, one of
  "none" (default), "emmeans", or "bootES" (#227, @rempsyc).

• The predict() argument for estimate_means() gets an "inverse_link" option,
  to calculate predictions on the link-scale and back-transform them to the
  response scale after aggregation by groups.

• estimate_means(), estimate_slopes() and estimate_contrasts() get a
  keep_iterations argument, to keep all posterior draws from Bayesian models
  added as columns to the output.

• New functions pool_predictions() and pool_contrasts(), to deal with
  modelbased objects that were applied to imputed data sets. E.g., functions
  like estimate_means() can be run on several data sets where missing values
  were imputed, and the multiple results from estimate_means() can be pooled
  using pool_predictions().

• The print() method is now explicitly documented and gets some new options
  to customize the output for tables.

• estimate_grouplevel() gets a new option, type = "total", to return the
  sum of fixed and random effects (similar to what coef() returns for (Bayesian)
  mixed models).

• New option "esarey" for the p_adjust argument. The "esarey" option is
  specifically for the case of Johnson-Neyman intervals, i.e. when calling
  estimate_slopes() with two numeric predictors in an interaction term.

• print_html() and print_md() pass ... to format-methods (e.g. to
  insight::format_table()), to tweak the output.

• The show_data argument in plot() is automatically set to FALSE when
  the models has a transformed response variable, but predictions were not
  back-transformed using the transform argument.

• The plot() method gets a numeric_as_discrete argument, to decide whether
  numeric predictors should be treated as factor or continuous, based on the
  of unique values in numeric predictors.

• Plots now use a probability scale for the y-axis for models whose response
  scale are probabilities (e.g., logistic regression).

• Improved printing for estimate_contrasts() when one of the focal predictors
  was numeric.

Bug fixes

• Fixed issue in the summary() method for estimate_slopes().

• Fixed issues with multivariate response models.

• Fixed issues with plotting ordinal or multinomial models.

• Fixed issues with ci argument, which was ignored for Bayesian models.

• Fixed issues with contrasting slopes when backend was "emmeans".

• Fixed issues in estimate_contrasts() when filtering numeric values in by.

• Fixed issues in estimate_grouplevel().

• Fixed issue in estimate_slopes() for models from package lme4.