- New function
`dominance_analysis()`

, to compute dominance analysis statistics and designations.

- 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.

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*.

It is now possible to hide messages about CI method below tables by specifying

`options("parameters_cimethod" = FALSE)`

(#722). By default, these messages are displayed.`model_parameters()`

now supports objects from package*marginaleffects*and objects returned by`car::linearHypothesis()`

.Added

`predict()`

method to`cluster_meta`

objects.Reorganization of docs for

`model_parameters()`

.

`model_parameters()`

now also includes standard errors and confidence intervals for slope-slope-correlations of random effects variances.`model_parameters()`

for mixed models gains a`ci_random`

argument, to toggle whether confidence intervals for random effects parameters should also be computed. Set to`FALSE`

if calculation of confidence intervals for random effects parameters takes too long.`ci()`

for*glmmTMB*models with`method = "profile"`

is now more robust.

Fixed issue with

*glmmTMB*models when calculating confidence intervals for random effects failed due to singular fits.`display()`

now correctly includes custom text and additional information in the footer (#722).Fixed issue with argument

`column_names`

in`compare_parameters()`

when strings contained characters that needed to be escaped for regular expressions.Fixed issues with unknown arguments in

`model_parameters()`

for*lavaan*models when`standardize = TRUE`

.

`model_parameters()`

now no longer treats data frame inputs as posterior samples. Rather, for data frames, now`NULL`

is returned. If you want to treat a data frame as posterior samples, set the new argument`as_draws = TRUE`

.

`sort_parameters()`

to sort model parameters by coefficient values.`standardize_parameters()`

,`standardize_info()`

and`standardise_posteriors()`

to standardize model parameters.

`model_parameters()`

`model_parameters()`

for mixed models from package*lme4*now also reports confidence intervals for random effect variances by default. Formerly, CIs were only included when`ci_method`

was`"profile"`

or`"boot"`

. The*merDeriv*package is required for this feature.`model_parameters()`

for`htest`

objects now also supports models from`var.test()`

.Improved support for

`anova.rms`

models in`model_parameters()`

.`model_parameters()`

now supports`draws`

objects from package*posterior*and`deltaMethods`

objects from package*car*.`model_parameters()`

now checks arguments and informs the user if specific given arguments are not supported for that model class (e.g.,`"vcov"`

is currently not supported for models of class*glmmTMB*).

The

`vcov`

argument, used for computing robust standard errors, did not calculate the correct p-values and confidence intervals for models of class`lme`

.`pool_parameters()`

did not save all relevant model information as attributes.`model_parameters()`

for models from package*glmmTMB*did not work when`exponentiate = TRUE`

and model contained a dispersion parameter that was different than sigma. Furthermore, exponentiating falsely exponentiated the dispersion parameter.

Added options to set defaults for different arguments. Currently supported:

`options("parameters_summary" = TRUE/FALSE)`

, which sets the default value for the`summary`

argument in`model_parameters()`

for non-mixed models.`options("parameters_mixed_summary" = TRUE/FALSE)`

, which sets the default value for the`summary`

argument in`model_parameters()`

for mixed models.

Minor improvements for

`print()`

methods.Robust uncertainty estimates:

- The
`vcov_estimation`

,`vcov_type`

, and`robust`

arguments are deprecated in these functions:`model_parameters()`

,`parameters()`

,`standard_error()`

,`p_value()`

, and`ci()`

. They are replaced by the`vcov`

and`vcov_args`

arguments. - The
`standard_error_robust()`

and`p_value_robust()`

functions are superseded by the`vcov`

and`vcov_args`

arguments of the`standard_error()`

and`p_value()`

functions. - Vignette: https://easystats.github.io/parameters/articles/model_parameters_robust.html

- The

Fixed minor issues and edge cases in

`n_clusters()`

and related cluster functions.Fixed issue in

`p_value()`

that returned wrong p-values for`fixest::feols()`

.

Improved speed performance for

`model_parameters()`

, in particular for glm’s and mixed models where random effect variances were calculated.Added more options for printing

`model_parameters()`

. See also revised vignette: https://easystats.github.io/parameters/articles/model_parameters_print.html

`model_parameters()`

`model_parameters()`

for mixed models gains an`include_sigma`

argument. If`TRUE`

, adds the residual variance, computed from the random effects variances, as an attribute to the returned data frame. Including sigma was the default behaviour, but now defaults to`FALSE`

and is only included when`include_sigma = TRUE`

, because the calculation was very time consuming.`model_parameters()`

for`merMod`

models now also computes CIs for the random SD parameters when`ci_method="boot"`

(previously, this was only possible when`ci_method`

was`"profile"`

).`model_parameters()`

for`glmmTMB`

models now computes CIs for the random SD parameters. Note that these are based on a Wald-z-distribution.Similar to

`model_parameters.htest()`

, the`model_parameters.BFBayesFactor()`

method gains`cohens_d`

and`cramers_v`

arguments to control if you need to add frequentist effect size estimates to the returned summary data frame. Previously, this was done by default.Column name for coefficients from

*emmeans*objects are now more specific.`model_prameters()`

for`MixMod`

objects (package*GLMMadaptive*) gains a`robust`

argument, to compute robust standard errors.

Fixed bug with

`ci()`

for class`merMod`

when`method="boot"`

.Fixed issue with correct association of components for ordinal models of classes

`clm`

and`clm2`

.Fixed issues in

`random_parameters()`

and`model_parameters()`

for mixed models without random intercept.Confidence intervals for random parameters in

`model_parameters()`

failed for (some?)`glmer`

models.Fix issue with default

`ci_type`

in`compare_parameters()`

for Bayesian models.

Following functions were moved to the new

*datawizard*package and are now re-exported from*parameters*package:`center()`

`convert_data_to_numeric()`

`data_partition()`

`demean()`

(and its aliases`degroup()`

and`detrend()`

)`kurtosis()`

`rescale_weights()`

`skewness()`

`smoothness()`

Note that these functions will be removed in the next release of
*parameters* package and they are currently being re-exported
only as a convenience for the package developers. This release should
provide them with time to make the necessary changes before this
breaking change is implemented.

Following functions were moved to the

*performance*package:`check_heterogeneity()`

`check_multimodal()`

The handling to approximate the degrees of freedom in

`model_parameters()`

,`ci()`

and`p_value()`

was revised and should now be more consistent. Some bugs related to the previous computation of confidence intervals and p-values have been fixed. Now it is possible to change the method to approximate degrees of freedom for CIs and p-values using the`ci_method`

, resp.`method`

argument. This change has been documented in detail in`?model_parameters`

, and online here: https://easystats.github.io/parameters/reference/model_parameters.htmlMinor changes to

`print()`

for*glmmTMB*with dispersion parameter.Added vignette on printing options for model parameters.

`model_parameters()`

The

`df_method`

argument in`model_parameters()`

is deprecated. Please use`ci_method`

now.`model_parameters()`

with`standardize = "refit"`

now returns random effects from the standardized model.`model_parameters()`

and`ci()`

for`lmerMod`

models gain a`"residuals"`

option for the`ci_method`

(resp.`method`

) argument, to explicitly calculate confidence intervals based on the residual degrees of freedom, when present.`model_parameters()`

supports following new objects:`trimcibt`

,`wmcpAKP`

,`dep.effect`

(in*WRS2*package),`systemfit`

`model_parameters()`

gains a new argument`table_wide`

for ANOVA tables. This can be helpful for users who may wish to report ANOVA table in wide format (i.e., with numerator and denominator degrees of freedom on the same row).`model_parameters()`

gains two new arguments,`keep`

and`drop`

.`keep`

is the new names for the former`parameters`

argument and can be used to filter parameters. While`keep`

selects those parameters whose names match the regular expression pattern defined in`keep`

,`drop`

is the counterpart and excludes matching parameter names.When

`model_parameters()`

is called with`verbose = TRUE`

, and`ci_method`

is not the default value, the printed output includes a message indicating which approximation-method for degrees of freedom was used.`model_parameters()`

for mixed models with`ci_method = "profile`

computes (profiled) confidence intervals for both fixed and random effects. Thus,`ci_method = "profile`

allows to add confidence intervals to the random effect variances.`model_parameters()`

should longer fail for supported model classes when robust standard errors are not available.

`n_factors()`

the methods based on fit indices have been fixed and can be included separately (`package = "fit"`

). Also added a`n_max`

argument to crop the output.`compare_parameters()`

now also accepts a list of model objects.`describe_distribution()`

gets`verbose`

argument to toggle warnings and messages.`format_parameters()`

removes dots and underscores from parameter names, to make these more “human readable”.The experimental calculation of p-values in

`equivalence_test()`

was replaced by a proper calculation p-values. The argument`p_value`

was removed and p-values are now always included.Minor improvements to

`print()`

,`print_html()`

and`print_md()`

.

The random effects returned by

`model_parameters()`

mistakenly displayed the residuals standard deviation as square-root of the residual SD.Fixed issue with

`model_parameters()`

for*brmsfit*objects that model standard errors (i.e. for meta-analysis).Fixed issue in

`model_parameters`

for`lmerMod`

models that, by default, returned residual degrees of freedom in the statistic column, but confidence intervals were based on`Inf`

degrees of freedom instead.Fixed issue in

`ci_satterthwaite()`

, which used`Inf`

degrees of freedom instead of the Satterthwaite approximation.Fixed issue in

`model_parameters.mlm()`

when model contained interaction terms.Fixed issue in

`model_parameters.rma()`

when model contained interaction terms.Fixed sign error for

`model_parameters.htest()`

for objects created with`t.test.formula()`

(issue #552)Fixed issue when computing random effect variances in

`model_parameters()`

for mixed models with categorical random slopes.

`check_sphericity()`

has been renamed into`check_sphericity_bartlett()`

.Removed deprecated arguments.

`model_parameters()`

for bootstrapped samples used in*emmeans*now treats the bootstrap samples as samples from posterior distributions (Bayesian models).

`SemiParBIV`

(*GJRM*),`selection`

(*sampleSelection*),`htest`

from the*survey*package,`pgmm`

(*plm*).

- Performance improvements for models from package
*survey*.

- Added a
`summary()`

method for`model_parameters()`

, which is a convenient shortcut for`print(..., select = "minimal")`

.

`model_parameters()`

`model_parameters()`

gains a`parameters`

argument, which takes a regular expression as string, to select specific parameters from the returned data frame.`print()`

for`model_parameters()`

and`compare_parameters()`

gains a`groups`

argument, to group parameters in the output. Furthermore,`groups`

can be used directly as argument in`model_parameters()`

and`compare_parameters()`

and will be passed to the`print()`

method.`model_parameters()`

for ANOVAs now saves the type as attribute and prints this information as footer in the output as well.`model_parameters()`

for*htest*-objects now saves the alternative hypothesis as attribute and prints this information as footer in the output as well.`model_parameters()`

passes arguments`type`

,`parallel`

and`n_cpus`

down to`bootstrap_model()`

when`bootstrap = TRUE`

.

`bootstrap_models()`

for*merMod*and*glmmTMB*objects gains further arguments to set the type of bootstrapping and to allow parallel computing.`bootstrap_parameters()`

gains the`ci_method`

type`"bci"`

, to compute bias-corrected and accelerated bootstrapped intervals.`ci()`

for`svyglm`

gains a`method`

argument.

Fixed issue in

`model_parameters()`

for*emmGrid*objects with Bayesian models.Arguments

`digits`

,`ci_digits`

and`p_digits`

were ignored for`print()`

and only worked when used in the call to`model_parameters()`

directly.

- Revised and improved the
`print()`

method for`model_parameters()`

.

`blrm`

(*rmsb*),`AKP`

,`med1way`

,`robtab`

(*WRS2*),`epi.2by2`

(*epiR*),`mjoint`

(*joineRML*),`mhurdle`

(*mhurdle*),`sarlm`

(*spatialreg*),`model_fit`

(*tidymodels*),`BGGM`

(*BGGM*),`mvord`

(*mvord*)

`model_parameters()`

`model_parameters()`

for`blavaan`

models is now fully treated as Bayesian model and thus relies on the functions from*bayestestR*(i.e. ROPE, Rhat or ESS are reported) .The

`effects`

-argument from`model_parameters()`

for mixed models was revised and now shows the random effects variances by default (same functionality as`random_parameters()`

, but mimicking the behaviour from`broom.mixed::tidy()`

). When the`group_level`

argument is set to`TRUE`

, the conditional modes (BLUPs) of the random effects are shown.`model_parameters()`

for mixed models now returns an`Effects`

column even when there is just one type of “effects”, to mimic the behaviour from`broom.mixed::tidy()`

. In conjunction with`standardize_names()`

users can get the same column names as in`tidy()`

for`model_parameters()`

objects.`model_parameters()`

for t-tests now uses the group values as column names.`print()`

for`model_parameters()`

gains a`zap_small`

argument, to avoid scientific notation for very small numbers. Instead,`zap_small`

forces to round to the specified number of digits.To be internally consistent, the degrees of freedom column for

`lqm(m)`

and`cgam(m)`

objects (with*t*-statistic) is called`df_error`

.`model_parameters()`

gains a`summary`

argument to add summary information about the model to printed outputs.Minor improvements for models from

*quantreg*.`model_parameters`

supports rank-biserial, rank epsilon-squared, and Kendall’s*W*as effect size measures for`wilcox.test()`

,`kruskal.test`

, and`friedman.test`

, respectively.

`describe_distribution()`

gets a`quartiles`

argument to include 25th and 75th quartiles of a variable.

Fixed issue with non-initialized argument

`style`

in`display()`

for`compare_parameters()`

.Make

`print()`

for`compare_parameters()`

work with objects that have “simple” column names for confidence intervals with missing CI-level (i.e. when column is named`"CI"`

instead of, say,`"95% CI"`

).Fixed issue with

`p_adjust`

in`model_parameters()`

, which did not work for adjustment-methods`"BY"`

and`"BH"`

.Fixed issue with

`show_sigma`

in`print()`

for`model_parameters()`

.Fixed issue in

`model_parameters()`

with incorrect order of degrees of freedom.

Roll-back R dependency to R >= 3.4.

Bootstrapped estimates (from

`bootstrap_model()`

or`bootstrap_parameters()`

) can be passed to`emmeans`

to obtain bootstrapped estimates, contrasts, simple slopes (etc) and their CIs.- These can then be passed to
`model_parameters()`

and related functions to obtain standard errors, p-values, etc.

- These can then be passed to

`model_parameters()`

now always returns the confidence level for as additional`CI`

column.The

`rule`

argument in`equivalenct_test()`

defaults to`"classic"`

.

`crr`

(*cmprsk*),`leveneTest()`

(*car*),`varest`

(*vars*),`ergm`

(*ergm*),`btergm`

(*btergm*),`Rchoice`

(*Rchoice*),`garch`

(*tseries*)

`compare_parameters()`

(and its alias`compare_models()`

) to show / print parameters of multiple models in one table.

Estimation of bootstrapped

*p*-values has been re-written to be more accurate.`model_parameters()`

for mixed models gains an`effects`

-argument, to return fixed, random or both fixed and random effects parameters.Revised printing for

`model_parameters()`

for*metafor*models.`model_parameters()`

for*metafor*models now recognized confidence levels specified in the function call (via argument`level`

).Improved support for effect sizes in

`model_parameters()`

from*anova*objects.

Fixed edge case when formatting parameters from polynomial terms with many degrees.

Fixed issue with random sampling and dropped factor levels in

`bootstrap_model()`

.