parameter 0.22.2

New supported models

• Support for models glm_weightit, multinom_weightit and ordinal_weightit
  from package WeightIt.

Changes

• Added p_significance() methods for frequentist models.

• Methods for degrees_of_freedom() have been removed. degrees_of_freedom()
  now calls insight::get_df().

• model_parameters() for data frames and draws objects from package
  posterior also gets an exponentiate argument.

Bug fixes

• Fixed issue with warning for spuriously high coefficients for Stan-models
  (non-Gaussian).

parameters 0.22.1

Breaking changes

• Revised calculation of the second generation p-value (SGPV) in equivalence_test(),
  which should now be more accurate related to the proportion of the interval
  that falls inside the ROPE. Formerly, the confidence interval was simply treated
  as uniformly distributed when calculating the SGPV, now the interval is assumed
  to be normally distributed.

New supported models

• Support for svy2lme models from package svylme.

Changes

• standardize_parameters() now also prettifies labels of factors.

Bug fixes

• Fixed issue with equivalence_test() when ROPE range was not symmetrically
  centered around zero (e.g., range = c(-99, 0.1)).

• model_parameters() for anova() from mixed models now also includes the
  denominator degrees of freedom in the output (df_error).

• print(..., pretty_names = "labels") for tobit-models from package AER now
  include value labels, if available.

• Patch release, to ensure that performance runs with older version of datawizard
  on Mac OS X with R (old-release).

parameters 0.22.0

Breaking changes

• Deprecated arguments in model_parameters() for htest, aov and
  BFBayesFactor objects were removed.

• Argument effectsize_type is deprecated. Please use es_type now. This change
  was necessary to avoid conflicts with partial matching of argument names (here:
  effects).

New supported models

• Support for objects from stats::Box.test().

• Support for glmgee models from package glmtoolbox.

Bug fix

• Fixed edge case in predict() for factor_analysis().

• Fixed wrong ORCID in DESCRIPTION.

parameters 0.21.7

Changes

• Fixed issues related to latest release from marginaleffects.

Bug fixes

• Fixes issue in compare_parameters() for models from package blme.

• Fixed conflict in model_parameters() when both include_reference = TRUE and
  pretty_names = "labels" were used. Now, pretty labels are correctly updated
  and preserved.

parameters 0.21.6

New supported models

• Support for models of class serp (serp).

Changes

• include_reference can now directly be set to TRUE in model_parameters()
  and doesn't require a call to print() anymore.

• compare_parameters() gains a include_reference argument, to add the
  reference category of categorical predictors to the parameters table.

• print_md() for compare_parameters() now by default uses the tinytable
  package to create markdown tables. This allows better control for column
  heading spanning over multiple columns.

Bug fixes

• Fixed issue with parameter names for model_parameters() and objects from
  package epiR.

• Fixed issue with exponentiate = TRUE for model_parameters() with models
  of class clmm (package ordinal), when model had no component column
  (e.g., no scale or location parameters were returned).

• include_reference now also works when factor were created "on-the-fly" inside
  the model formula (i.e. y ~ as.factor(x)).

parameters 0.21.5

Bug fixes

• Fixes CRAN check errors related to the changes in the latest update of
  marginaleffects.

parameters 0.21.4

Breaking changes

• The exponentiate argument of model_parameters() for
  marginaleffects::predictions() now defaults to FALSE, in line with all
  the other model_parameters() methods.

Changes

• model_parameters() for models of package survey now gives informative
  messages when bootstrap = TRUE (which is currently not supported).

• n_factors() now also returns the explained variance for the number of
  factors as attributes.

• model_parameters() for objects of package metafor now warns when unsupported
  arguments (like vcov) are used.

• Improved documentation for pool_parameters().

Bug fixes

• print(include_reference = TRUE) for model_parameters() did not work when
  run inside a pipe-chain.

• Fixed issues with format() for objects returned by compare_parameters()
  that included mixed models.

parameters 0.21.3

Changes

• principal_components() and factor_analysis() now also work when argument
  n = 1.

• print_md() for compare_parameters() now gains more arguments, similar to
  the print() method.

• bootstrap_parameters() and model_parameters() now accept bootstrapped
  samples returned by bootstrap_model().

• The print() method for model_parameters() now also yields a warning for
  models with logit-links when possible issues with (quasi) complete separation
  occur.

Bug fixes

• Fixed issue in print_html() for objects from package ggeffects.

• Fixed issues for nnet::multinom() with wide-format response variables (using
  cbind()).

• Minor fixes for print_html() method for model_parameters().

• Robust standard errors (argument vcov) now works for plm models.

parameters 0.21.2

Changes

• Minor improvements to factor analysis functions.

• The ci_digits argument of the print() method for model_parameters() now
  defaults to the same value of digits.

• model_parameters() for objects from package marginaleffects now also
  accepts the exponentiate argument.

• The print(), print_html(), print_md() and format() methods for
  model_parameters() get an include_reference argument, to add the reference
  category of categorical predictors to the parameters table.

Bug fixes

• Fixed issue with wrong calculation of test-statistic and p-values in
  model_parameters() for fixest models.

• Fixed issue with wrong column header for glm models with
  family = binomial("identiy").

• Minor fixes for dominance_analysis().

parameters 0.21.1

General

• Added support for models of class nestedLogit (nestedLogit).

Changes to functions

• model_parameters() now also prints correct "pretty names" when predictors
  where converted to ordered factors inside formulas, e.g. y ~ as.ordered(x).

• model_parameters() now prints a message when the vcov argument is provided
  and ci_method is explicitly set to "profile". Else, when vcov is not
  NULL and ci_method is NULL, it defaults to "wald", to return confidence
  intervals based on robust standard errors.