ggeffects 1.7.0

Breaking

• The deprecated argument ci.lvl in test_predictions() was removed.

General

• ggpredict() now supports models of class glmgee (package glmtoolbox).

• ggemmeans() gains arguments vcov_fun, vcov_type and vcov_args to
  specify the variance-covariance matrix for the marginal means, similar to
  what is already available in ggpredict() and ggaverage().

• When test = "contrast", the engine is automatically set to "emmeans"
  in test_predictions().

• ggaverage() (or predict_response(..., margin = "empirical")) now also
  supports following type options for zero-inflated models: "zi_prob",
  "zero_inflated" and "fixed".

• Support for zero-inflated models was massively improved in test_predictions(),
  which, for instance, now also supports scale = "zprob" to calculate contrasts
  for the zero-inflation probability for zero-inflated models from glmmTMB or
  pscl. Furthermore, when predictions for zero-inflation probabilities were
  calculated using pr <- predict_response(..., type = "zi_prob"), corresponding
  contrasts will be calculated with test_predictions(pr) automatically.
  Additionally, other types for models with zero-inflation component
  ("zero_inflated", "fixed") are supported as well.

• ggeffect() now warns the user about arguments that are supported by
  ggpredict() or ggemmeans(), but not by this function (e.g., vcov_fun).

• Improved accuracy of standard errors for test_predictions(..., engine = "ggeffects").

• The terms argument now also accepts the shortcut "percentile" (plus numeric
  value) to select a range of percentiles for continuous variables, e.g.
  terms = "x [percentile90]" to select a range of the 90% percentile.

Bug fixes

• Fixed issue with brms models with monotonic effects in formula (mo()).

• Fixed issue in vcov() for ggeffects objects, which could occur in rare
  situations when some of the predictors were character vectors.

• Fixed issue with calculation of standard errors when one of the focal term
  was a character vector.

• Fixed issue in plot() method with show_data = TRUE, where in certain
  situations the raw data points were not colored when groups were present.

• Fixed issue in plot() with too many data points when collapsing random
  effects groups.