*sjstats* is being re-structured, and many functions are re-implemented in new packages that are part of a new project called **easystats**.

Therefore, following functions are now deprecated:

`cohens_f()`

, please use`effectsize::cohens_f()`

.`std_beta()`

, please use`effectsize::standardize_parameters()`

.`tidy_stan()`

, please use`parameters::model_parameters()`

.`scale_weights()`

, please use`parameters::rescale_weights()`

.`robust()`

, please use`parameters::standard_error_robust()`

.

- Functions for weighted statistics with prefix
`wtd_*()`

have been renamed to`weighted_*()`

. `svy_md()`

was renamed to`survey_median()`

.`mannwhitney()`

is an alias for`mwu()`

.`means_by_group()`

is an alias for`grpmean()`

.

*sjstats* is being re-structured, and many functions are re-implemented in new packages that are part of a new project called **easystats**. The aim of **easystats** is to provide a unifying and consistent framework to tame, discipline and harness the scary R statistics and their pesky models.

Therefore, following functions are now deprecated:

`p_value()`

, please use`parameters::p_value()`

`se()`

, please use`parameters::standard_error()`

- Revise some functions to cope with the forthcoming
*insight*update.

- Minor revisions to meet the changes in the forthcoming update from
*tidyr*. `design_effect()`

is an alias for`deff()`

.`samplesize_mixed()`

is an alias for`smpsize_lmm()`

.`crosstable_statistics()`

is an alias for`xtab_statistics()`

.

`svyglm.zip()`

to fit zero-inflated Poisson models for survey-designs.

`phi()`

and`cramer()`

can now compute confidence intervals.`tidy_stan()`

removes prior parameters from output.`tidy_stan()`

now also prints the probability of direction.

- Fix bug with wrong computation in
`odds_to_rr()`

.

`epsilon_sq()`

, to compute epsilon-squared effect-size.

*sjstats* is being re-structured, and many functions are re-implemented in new packages that are part of a new project called **easystats**. The aim of **easystats** is to provide a unifying and consistent framework to tame, discipline and harness the scary R statistics and their pesky models.

Therefore, following functions are now deprecated:

`link_inverse()`

, please use`insight::link_inverse()`

`model_family()`

, please use`insight::model_info()`

`model_frame()`

, please use`insight::get_data()`

`pred_vars()`

, please use`insight::find_predictors()`

`re_grp_var()`

, please use`insight::find_random()`

`grp_var()`

, please use`insight::find_random()`

`resp_val()`

, please use`insight::get_response()`

`resp_var()`

, please use`insight::find_response()`

`var_names()`

, please use`insight::clean_names()`

`overdisp()`

, please use`performance::check_overdispersion()`

`zero_count()`

, please use`performance::check_zeroinflation()`

`converge_ok()`

, please use`performance::check_convergence()`

`is_singular()`

, please use`performance::check_singularity()`

`reliab_test()`

, please use`performance::item_reliability()`

`split_half()`

, please use`performance::item_split_half()`

`predictive_accurarcy()`

, please use`performance::performance_accuracy()`

`cronb()`

, please use`performance::cronbachs_alpha()`

`difficulty()`

, please use`performance::item_difficulty()`

`mic()`

, please use`performance::item_intercor()`

`pca()`

, please use`parameters::principal_components()`

`pca_rotate()`

, please use`parameters::principal_components()`

`r2()`

, please use`performance::r2()`

`icc()`

, please use`performance::icc()`

`rmse()`

, please use`performance::rmse()`

`rse()`

, please use`performance::rse()`

`mse()`

, please use`performance::mse()`

`hdi()`

, please use`bayestestR::hdi()`

`cred_int()`

, please use`bayestestR::ci()`

`rope()`

, please use`bayestestR::rope()`

`n_eff()`

, please use`bayestestR::effective_sample()`

`equi_test()`

, please use`bayestestR::equivalence_test()`

`multicollin()`

, please use`performance::check_collinearity()`

`normality()`

, please use`performance::check_normality()`

`autocorrelation()`

, please use`performance::check_autocorrelation()`

`heteroskedastic()`

, please use`performance::check_heteroscedasticity()`

`outliers()`

, please use`performance::check_outliers()`

- Anova-stats functions (like
`eta_sq()`

) get a`method`

-argument to define the method for computing confidence intervals from bootstrapping.

- In some situations,
`smpsize_lmm()`

could result in negative sample-size recommendations. This was fixed, and a warning is now shown indicating that the parameters for the power-calculation should be modified. - Fixed issue with wrong calculated effect size
`r`

in`mwu()`

if group-factor contained more than two groups.

- Following models/objects are now supported by model-information functions like
`model_family()`

,`link_inverse()`

or`model_frame()`

:`MixMod`

(package**GLMMadaptive**),**MCMCglmm**,`mlogit`

and`gmnl`

. - Reduce package dependencies.

`cred_int()`

, to compute uncertainty intervals of Bayesian models. Mimics the behaviour and style of`hdi()`

and is thus a convenient complement to functions like`posterior_interval()`

.

`equi_test()`

now finds better defaults for models with binomial outcome (like logistic regression models).`r2()`

for mixed models now also should work properly for mixed models fitted with**rstanarm**.`anova_stats()`

and alike (e.g.`eta_sq()`

) now all preserve original term names.`model_family()`

now returns`$is_count = TRUE`

, when model is a count-model, and`$is_beta = TRUE`

for models with beta-family.`pred_vars()`

checks that return value has only unique values.`pred_vars()`

gets a`zi`

-argument to return the variables from a model’s zero-inflation-formula.

- Fix minor issues in
`wtd_sd()`

and`wtd_mean()`

when weight was`NULL`

(which usually shoudln’t be the case anyway). - Fix potential issue with
`deparse()`

, cutting off very long formulas in various functions. - Fix encoding issues in help-files.

- Export
`dplyr::n()`

, to meet forthcoming changes in dplyr 0.8.0.

`boot_ci()`

gets a`ci.lvl`

-argument.- The
`rotation`

-argument in`pca_rotate()`

now supports all rotations from`psych::principal()`

. `pred_vars()`

gets a`fe.only`

-argument to return only fixed effects terms from mixed models, and a`disp`

-argument to return the variables from a model’s dispersion-formula.`icc()`

for Bayesian models gets a`adjusted`

-argument, to calculate adjusted and conditional ICC (however, only for Gaussian models).- For
`icc()`

for non-Gaussian Bayes-models, a message is printed that recommends setting argument`ppd`

to`TRUE`

. `resp_val()`

and`resp_var()`

now also work for**brms**-models with additional response information (like`trial()`

in formula).`resp_var()`

gets a`combine`

-argument, to return either the name of the matrix-column or the original variable names for matrix-columns.`model_frame()`

now also returns the original variables for matrix-column-variables.`model_frame()`

now also returns the variable from the dispersion-formula of**glmmTMB**-models.`model_family()`

and`link_inverse()`

now supports**glmmPQL**,**felm**and**lm_robust**-models.`anova_stats()`

and alike (`omeqa_sq()`

etc.) now support gam-models from package**gam**.`p_value()`

now supports objects of class`svyolr`

.

- Fix issue with
`se()`

and`get_re_var()`

for objects returned by`icc()`

. - Fix issue with
`icc()`

for Stan-models. `var_names()`

did not clear terms with log-log transformation, e.g.`log(log(y))`

.- Fix issue in
`model_frame()`

for models with splines with only one column.

- Revised help-files for
`r2()`

and`icc()`

, also by adding more references.

`re_grp_var()`

to find group factors of random effects in mixed models.

`omega_sq()`

and`eta_sq()`

give more informative messages when using non-supported objects.`r2()`

and`icc()`

give more informative warnings and messages.`tidy_stan()`

supports printing simplex parameters of monotonic effects of**brms**models.`grpmean()`

and`mwu()`

get a`file`

and`encoding`

argument, to save the HTML output as file.

`model_frame()`

now correctly names the offset-columns for terms provided as`offset`

-argument (i.e. for models where the offset was not specified inside the formula).- Fixed issue with
`weights`

-argument in`grpmean()`

when variable name was passed as character vector. - Fixed issue with
`r2()`

for**glmmTMB**models with`ar1`

random effects structure.

`wtd_chisqtest()`

to compute a weighted Chi-squared test.`wtd_median()`

to compute the weighted median of variables.`wtd_cor()`

to compute weighted correlation coefficients of variables.

`mediation()`

can now cope with models from different families, e.g. if the moderator or outcome is binary, while the treatment-effect is continuous.`model_frame()`

,`link_inverse()`

,`pred_vars()`

,`resp_var()`

,`resp_val()`

,`r2()`

and`model_family()`

now support`clm2`

-objects from package**ordinal**.`anova_stats()`

gives a more informative message for non-supported models or ANOVA-options.

- Fixed issue with
`model_family()`

and`link_inverse()`

for models fitted with`pscl::hurdle()`

or`pscl::zeroinfl()`

. - Fixed issue with wrong title in
`grpmean()`

for grouped data frames, when grouping variable was an unlabelled factor. - Fix issue with
`model_frame()`

for**coxph**-models with polynomial or spline-terms. - Fix issue with
`mediation()`

for logical variables.

- Reduce package dependencies.

`wtd_ttest()`

to compute a weighted t-test.`wtd_mwu()`

to compute a weighted Mann-Whitney-U or Kruskal-Wallis test.

`robust()`

was revised, getting more arguments to specify different types of covariance-matrix estimation, and handling these more flexible.- Improved
`print()`

-method for`tidy_stan()`

for*brmsfit*-objects with categorical-families. `se()`

now also computes standard errors for relative frequencies (proportions) of a vector.`r2()`

now also computes r-squared values for*glmmTMB*-models from`genpois`

-families.`r2()`

gives more precise warnings for non-supported model-families.`xtab_statistics()`

gets a`weights`

-argument, to compute measures of association for contingency tables for weighted data.- The
`statistics`

-argument in`xtab_statistics()`

gets a`"fisher"`

-option, to force Fisher’s Exact Test to be used. - Improved variance calculation in
`icc()`

for generalized linear mixed models with Poisson or negative binomial families. `icc()`

gets an`adjusted`

-argument, to calculate the adjusted and conditional ICC for mixed models.- To get consistent argument names accross functions, argument
`weight.by`

is now deprecated and renamed into`weights`

.

- Fix issues with effect size computation for repeated-measure Anova when using bootstrapping to compute confidence intervals.
`grpmean()`

now also adjusts the`n`

-columm for weighted data.`icc()`

,`re_var()`

and`get_re_var()`

now correctly compute the random-effect-variances for models with multiple random slopes per random effect term (e.g.,`(1 + rs1 + rs2 | grp)`

).- Fix issues in
`tidy_stan()`

,`mcse()`

,`hdi()`

and`n_eff()`

for`stan_polr()`

-models. - Plotting
`equi_test()`

did not work for intercept-only models.

- The S3-generics for functions like
`hdi()`

,`rope()`

,`equi_test()`

etc. are now more generic, and function usage for each supported object is now included in the documentation. - Following functions are now S3-generic:
`icc()`

,`r2()`

,`p_value()`

,`se()`

, and`std_beta()`

. - Added
`print()`

-methods for some more functions, for a clearer output. - Revised
`r2()`

for mixed models (packages**lme4**,**glmmTMB**). The r-squared value should be much more precise now, and reports the marginal and conditional r-squared values. - Reduced package dependencies and removed
*apaTables*and*MBESS*from suggested packages `stanmvreg`

-models are now supported by many functions.

`binned_resid()`

to plot binned residuals for logistic regression models.`error_rate()`

to compute model quality for logistic regression models.`auto_prior()`

to quickly create automatically adjusted priors for brms-models.`difficulty()`

to compute the item difficulty.

`icc()`

gets a`ppd`

-argument for Stan-models (*brmsfit*and*stanreg*), which performs a variance decomposition based on the posterior predictive distribution. This is the recommended way for non-Gaussian models.- For Stan-models (
*brmsfit*and*stanreg*),`icc()`

now also computes the HDI for the ICC and random-effect variances. Use the`prob`

-argument to specify the limits of this interval. `link_inverse()`

and`model_family()`

now support*clmm*-models (package*ordinal*) and*glmRob*and*lmRob*-models (package*robust*).`model_family()`

gets a`multi.resp`

-argument, to return a list of family-informations for multivariate-response models (of class`brmsfit`

or`stanmvreg`

).`link_inverse()`

gets a`multi.resp`

-argument, to return a list of link-inverse-functions for multivariate-response models (of class`brmsfit`

or`stanmvreg`

).`p_value()`

now supports*rlm*-models (package*MASS*).`check_assumptions()`

for single models with`as.logical = FALSE`

now has a nice print-method.`eta_sq()`

and`omega_sq()`

now also work for repeated-measure Anovas, i.e. Anova with error term (requires broom > 0.4.5).

`model_frame()`

and`var_names()`

now correctly cleans nested patterns like`offset(log(x + 10))`

from column names.`model_frame()`

now returns proper column names from*gamm4*models.`model_frame()`

did not work when the model frame had spline-terms and weights.- Fix issue in
`robust()`

when`exponentiate = TRUE`

and`conf.int = FALSE`

. `reliab_test()`

returned an error when the provided data frame has less than three columns, instead of returning`NULL`

.

- Added new Vignette
*Statistics for Bayesian Models*.

`equi_test()`

to test if parameter values in Bayesian estimation should be accepted or rejected.`mediation()`

to print a summary of a mediation analysis from multivariate response models fitted with*brms*.

`link_inverse()`

now also returns the link-inverse function for cumulative-family*brms*-models.`model_family()`

now also returns an`is_ordinal`

-element with information if the model is ordinal resp. a cumulative link model.- Functions that access model information (like
`model_family()`

) now better support`vglm`

-models (package*VGAM*). `r2()`

now also calculates the standard error for*brms*or*stanreg*models.`r2()`

gets a`loo`

-argument to calculate LOO-adjusted rsquared values for*brms*or*stanreg*models. This measure comes conceptionally closer to an adjusted r-squared measure.- Effect sizes (
`anova_stats()`

,`eta_sq()`

etc.) are now also computed for mixed models. - To avoid confusion,
`n_eff()`

now computes the number of effective samples, and no longer its ratio in relation to the total number of samples. - The column name for the ratio of the number of effective samples in
`tidy_stan()`

is now named*neff_ratio*, to avoid confusion.

- Fixed issue in
`se()`

for`icc()`

-objects, where random effect term could not be found. - Fixed issue in
`se()`

for`merMod`

-objects. - Fixed issue in
`p_value()`

for mixed models with KR-approximation, which is now more accurate.