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.

parameters 0.21.0

Breaking Changes

• It is no longer possible to calculate Satterthwaite-approximated degrees of
  freedom for mixed models from package nlme. This was based on the
  lavaSearch2 package, which no longer seems to support models of class lme.

Changes to functions

• Improved support for objects of class mipo for models with ordinal or
  categorical outcome.

parameters 0.20.3

General

• Added support for models of class hglm (hglm), mblogit (mclogit),
  fixest_multi (fixest), and phylolm / phyloglm (phylolm).

• as.data.frame methods for extracting posterior draws via bootstrap_model()
  have been retired. Instead, directly using bootstrap_model() is recommended.

Changes to functions

• equivalence_test() gets a method for ggeffects objects from package
  ggeffects.

• equivalence_test() now prints the SGPV column instead of % in ROPE.
  This is because the former % in ROPE actually was equivalent to the second
  generation p-value (SGPV) and refers to the proportion of the range of the
  confidence interval that is covered by the ROPE. However, % in ROPE did
  not refer to the probability mass of the underlying distribution of a confidence
  interval that was covered by the ROPE, hence the old column name was a bit
  misleading.

• Fixed issue in model_parameters.ggeffects() to address forthcoming changes
  in the ggeffects package.

Bug fixes

• When an invalid or not supported value for the p_adjust argument in
  model_parameters() is provided, the valid options were not shown in correct
  capital letters, where appropriate.

• Fixed bug in cluster_analysis() for include_factors = TRUE.

• Fixed warning in model_parameters() and ci() for models from package
  glmmTMB when ci_method was either "profile" or "uniroot".

parameters 0.20.2

General

• Reduce unnecessary warnings.

• The deprecated argument df_method in model_parameters()was removed.

• Output from model_parameters() for objects returned by manova() and
  car::Manova() is now more consistent.

Bug fix

• Fixed issues in tests for mmrm models.

• Fixed issue in bootstrap_model() for models of class glmmTMB with
  dispersion parameters.

• Fixed failing examples.

parameters 0.20.1

General

• Added support for models of class flic and flac (logistf), mmrm (mmrm).

Changes

• model_parameters() now includes a Group column for stanreg or brmsfit
  models with random effects.

• The print() method for model_parameters() now uses the same pattern to
  print random effect variances for Bayesian models as for frequentist models.

Bug fix

• Fixed issue with the print() method for compare_parameters(), which
  duplicated random effects parameters rows in some edge cases.

• Fixed issue with the print() method for compare_parameters(), which
  didn't work properly when ci=NULL.

parameters 0.20.0

Breaking

• The deprecated argument df_method in model_parameters() is now defunct
  and throws an error when used.

• The deprecated functions ci_robust(), p_robust() and standard_error_robust
  have been removed. These were superseded by the vcov argument in ci(),
  p_value(), and standard_error(), respectively.

• The style argument in compare_parameters() was renamed into select.

New functions

• p_function(), to print and plot p-values and compatibility (confidence)
  intervals for statistical models, at different levels. This allows to see
  which estimates are most compatible with the model at various compatibility
  levels.

• p_calibrate(), to compute calibrated p-values.

Changes

• model_parameters() and compare_parameters() now use the unicode character
  for the multiplication-sign as interaction mark (i.e. \u00d7). Use
  options(parameters_interaction = <value>) or the argument interaction_mark
  to use a different character as interaction mark.

• The select argument in compare_parameters(), which is used to control the
  table column elements, now supports an experimental glue-like syntax.
  See this vignette Printing Model Parameters. Furthermore, the select
  argument can also be used in the print() method for model_parameters().

• print_html() gets a font_size and line_padding argument to tweak the
  appearance of HTML tables. Furthermore, arguments select and column_labels
  are new, to customize the column layout of tables. See examples in ?display.

• Consolidation of vignettes on standardization of model parameters.

• Minor speed improvements.

Bug fix

• model_parameters().BFBayesFactor no longer drops the BF column if the
  Bayes factor is NA.

• The print() and display() methods for model_parameters() from Bayesian
  models now pass the ... to insight::format_table(), allowing extra
  arguments to be recognized.

• Fixed footer message regarding the approximation method for CU and p-values
  for mixed models.

• Fixed issues in the print() method for compare_parameters() with mixed
  models, when some models contained within-between components (see
  wb_component) and others did not.

parameters 0.19.0

Breaking

• Arguments that calculate effectsize in model_parameters() for htest,
  Anova objects and objects of class BFBayesFactor were revised. Instead of
  single arguments for the different effectsizes, there is now one argument,
  effectsize_type. The reason behind this change is that meanwhile many
  new type of effectsizes have been added to the effectsize package, and
  the generic argument allows to make use of those effect sizes.

• The attribute name in PCA / EFA has been changed from data_set to dataset.

• The minimum needed R version has been bumped to 3.6.

• Removed deprecated argument parameters from model_parameters().

• standard_error_robust(), ci_robust() and p_value_robust() are now
  deprecated and superseded by the vcov and vcov_args arguments in the
  related methods standard_error(), ci() and p_value(), respectively.

• Following functions were moved from package parameters to performance:
  check_sphericity_bartlett(), check_kmo(), check_factorstructure() and
  check_clusterstructure().

Changes to functions

• Added sparse option to principal_components() for sparse PCA.

• The pretty_names argument from the print() method can now also be
  "labels", which will then use variable and value labels (if data is
  labelled) as pretty names. If no labels were found, default pretty names
  are used.

• bootstrap_model() for models of class glmmTMB and merMod gains a
  cluster argument to specify optional clusters when the parallel
  option is set to "snow".

• P-value adjustment (argument p_adjust in model_parameters()) is now
  performed after potential parameters were removed (using keep or drop),
  so adjusted p-values is only applied to the parameters of interest.

• Robust standard errors are now supported for fixest models with the vcov
  argument.

• print() for model_parameters() gains a footer argument, which can be
  used to suppress the footer in the output. Further more, if footer = ""
  or footer = FALSE in print_md(), no footer is printed.

• simulate_model() and simulate_parameters() now pass ... to
  insight::get_varcov(), to allow simulated draws to be based on
  heteroscedasticity consistent variance covariance matrices.

• The print() method for compare_parameters() was improved for models with
  multiple components (e.g., mixed models with fixed and random effects, or
  models with count- and zero-inflation parts). For these models,
  compare_parameters(effects = "all", component = "all") prints more nicely.

Bug fixes

• Fix erroneous warning for p-value adjustments when the differences between
  original and adjusted p-values were very small.

parameters 0.18.2

New functions

• New function dominance_analysis(), to compute dominance analysis
  statistics and designations.

Changes to functions

• Argument ci_random in model_parameters() defaults to NULL. It uses a
  heuristic to determine if random effects confidence intervals are likely to
  take a long time to compute, and automatically includes or excludes those
  confidence intervals. Set ci_random to TRUE or FALSE to explicitly
  calculate or omit confidence intervals for random effects.

Bug fixes

• Fix issues in pool_parameters() for certain models with special components
  (like MASS::polr()), that failed when argument component was set to
  "conditional" (the default).

• Fix issues in model_parameters() for multiple imputation models from
  package Hmisc.

parameters 0.8.3

Release for JOSS

parameters 0.8.2

on CRAN