## parameters 0.18.1

CRAN release: 2022-05-29

### General

• 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().

### Changes to functions

• 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.

### Bug fixes

• 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.

## parameters 0.18.0

CRAN release: 2022-05-24

### Breaking Changes

• 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.

### New functions

• sort_parameters() to sort model parameters by coefficient values.

• standardize_parameters(), standardize_info() and standardise_posteriors() to standardize model parameters.

### Changes to functions

#### 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).

### Bug fixes

• 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.

## parameters 0.17.0

CRAN release: 2022-03-10

### General

• 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

### Bug fixes

• 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().

## parameters 0.16.0

CRAN release: 2022-01-12

### Changes to functions

#### 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.

### Bug fixes

• 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.

## parameters 0.15.0

CRAN release: 2021-10-18

### Breaking changes

• 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()

### General

• 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.html

• Minor changes to print() for glmmTMB with dispersion parameter.

• Added vignette on printing options for model parameters.

### Changes to functions

#### 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.

#### Other functions

• 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().

### Bug fixes

• 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.

## parameters 0.14.0

CRAN release: 2021-05-29

### Breaking changes

• 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).

### New supported model classes

• SemiParBIV (GJRM), selection (sampleSelection), htest from the survey package, pgmm (plm).

### General

• Performance improvements for models from package survey.

### New functions

• Added a summary() method for model_parameters(), which is a convenient shortcut for print(..., select = "minimal").

### Changes to functions

#### 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.

#### other

• 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.

### Bug fixes

• 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.

## parameters 0.13.0

CRAN release: 2021-04-08

### General

• Revised and improved the print() method for model_parameters().

### New supported model classes

• blrm (rmsb), AKP, med1way, robtab (WRS2), epi.2by2 (epiR), mjoint (joineRML), mhurdle (mhurdle), sarlm (spatialreg), model_fit (tidymodels), BGGM (BGGM), mvord (mvord)

### Changes to functions

#### 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.

#### Other functions

• describe_distribution() gets a quartiles argument to include 25th and 75th quartiles of a variable.

### Bug fixes

• 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.

## parameters 0.12.0

CRAN release: 2021-02-21

### General

• 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.

### Breaking changes

• model_parameters() now always returns the confidence level for as additional CI column.

• The rule argument in equivalenct_test() defaults to "classic".

### New supported model classes

• crr (cmprsk), leveneTest() (car), varest (vars), ergm (ergm), btergm (btergm), Rchoice (Rchoice), garch (tseries)

### New functions

• compare_parameters() (and its alias compare_models()) to show / print parameters of multiple models in one table.

### Changes to functions

• 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.

### Bug fixes

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

• Fixed issue with random sampling and dropped factor levels in bootstrap_model().