- Remove unused imports.
- Cross refences from
`dplyr::select_helpers`

were updated to`tidyselect::select_helpers`

.

`var_names()`

now also cleans variable names from variables modeled with the`mi()`

function (multiple imputation on the fly in*brms*).`reliab_test()`

gets an`out`

-argument, to print output to console, or as HTML table in the viewer or web browser.

- Fix issues with
`mcse()`

,`n_eff()`

and`tidy_stan()`

with more complex*brmsfit*-models. - Fix issue in
`typical_value()`

to prevent error for R-oldrel-Windows. `model_frame()`

now returns response values from models, which are in matrix form (bound with`cbind()`

), as is.- Fixed issues in
`grpmean()`

, where values instead of value labels were printed if some categories were not present in the data.

- Beautiful colored output for
`grpmean()`

and`mwu()`

.

`mcse()`

to compute the Monte Carlo standard error for`stanreg`

- and`brmsfit`

-models.`n_eff()`

to compute the effective sample size for`stanreg`

- and`brmsfit`

-models.

`grpmean()`

now uses`contrasts()`

from package*emmeans*to compute p-values, which correclty indicate whether the sub-group mean is significantly different from the total mean.`grpmean()`

gets an`out`

-argument, to print output to console, or as HTML table in the viewer or web browser.`tidy_stan()`

now includes information on the Monte Carlo standard error.`model_frame()`

,`p_value()`

and`link_inverse()`

now support Zelig-relogit-models.`typical_value()`

gets an explicit`weight.by`

-argument.

`model_frame()`

did not work properly for variables that were standardized with`scale()`

.- In certain cases,
`weight.by`

-argument did not work in`grpmean()`

.

- Remove deprecated
`get_model_pval()`

. - Revised documentation for
`overdisp()`

.

`scale_weights()`

to rescale design weights for multilevel models.`pca()`

and`pca_rotate()`

to create tidy summaries of principal component analyses or rotated loadings matrices from PCA.`gmd()`

to compute Gini’s mean difference.`is_prime()`

to check whether a number is a prime number or not.

`link_inverse()`

now supports`brmsfit`

,`multinom`

and`clm`

-models.`p_value()`

now supports`polr`

and`multinom`

-models.`zero_count()`

gets a`tolerance`

-argument, to accept models with a ratio within a certain range of 1.`var_names()`

now also cleans variable names from variables modelled with the`offset()`

,`lag()`

or`diff()`

function.`icc()`

,`re_var()`

and`get_re_var()`

now support`brmsfit`

-objects (models fitted with the*brms*-package).- For
`fun = "weighted.mean"`

,`typical_value()`

now checks if vector of weights is of same length as`x`

. - The print-method for
`grpmean()`

now also prints the overall p-value from the model.

`resp_val()`

,`cv_error()`

and`pred_accuracy()`

did not work for formulas with transforming function for response terms, e.g.`log(response)`

.

- Fixed examples, to resolve issues with CRAN package checks.
- More model objects supported in
`p_value()`

.

`model_frame()`

to get the model frame from model objects, also of those models that don’t have a S3-generic model.frame-function.`var_names()`

to get cleaned variable names from model objects.`link_inverse()`

to get the inverse link function from model objects.

- The
`fun`

-argument in`typical_value()`

can now also be a named vector, to apply different functions for numeric and categorical variables.

- Fixed issue with specific model formulas in
`pred_vars()`

. - Fixed issue with specific model objects in
`resp_val()`

. - Fixed issue with nested models in
`re_var()`

.

`tidy_stan()`

to return a tidy summary of Stan-models.

`hdi()`

and`rope()`

now also work for`brmsfit`

-models, from package*brms*.`hdi()`

and`rope()`

now have a`type`

-argument, to return fixed, random or all effects for mixed effects models.

`typical_value()`

gets a “zero”-option for the`fun`

-argument.- Changes to
`icc()`

, which used`stats::sigma()`

and thus required R-version 3.3 or higher. Now should depend on R 3.2 again. `se()`

now also supports`stanreg`

and`stanfit`

objects.`hdi()`

now also supports`stanfit`

-objects.`std_beta()`

gets a`ci.lvl`

-argument, to specify the level of the calculated confidence interval for standardized coefficients.`get_model_pval()`

is now deprecated. Please use`p_value()`

instead.

`rope()`

to calculate the region of practical equivalence for MCMC samples.

- Added vignettes for various functions.
- Fixed issue with latest tidyr-update on CRAN.

`grpmean()`

to compute mean values by groups (One-way Anova).`hdi()`

to compute high density intervals (HDI) for MCMC samples.`find_beta()`

and`find_beta2()`

to find the shape parameters of a Beta distribution.`find_normal()`

and`find_cauchy()`

to find the parameters of a normal or cauchy distribution.

`typical_value()`

, to return the typical value of a variable.`eta_sq()`

,`cohens_f()`

and`omega_sq()`

to compute (partial) eta-squared or omega-squared statistics, or Cohen’s F for anova tables.`anova_stats()`

to compute a complete model summary, including (partial) eta-squared, omega-squared and Cohen’s F statistics for anova tables, returned as tidy data frame.`svy_md()`

as convenient shortcut to compute the median for variables from survey designs.`is_singular()`

to check a model fit for singularity in case of post-fitting convergence warnings.

- Computation of
`r2()`

for`glm`

-objects is now based on log-Likelihood methods and also accounts for count models. - Better
`print()`

-method for`overdisp()`

. `print()`

-method for`svyglm.nb()`

now also prints the dispersion parameter Theta.`overdisp()`

now supports`glmmTMB`

-objects.`boot_ci()`

also displays CI based on sample quantiles.

`std_beta()`

did not work for models with only one predictor.

`icc()`

,`re_var()`

and`get_re_var()`

now support`glmmTMB`

-objects.`pred_accuracy()`

now also reports the standard error of accuracy, and gets a print-method.

`pred_accuracy()`

with cross-validation-method did not correctly account for the generated test data.- Fixed issue with calculation in
`smpsize_lmm()`

and`se_ybar()`

.

- Revised imports: Labelled data functions from package
*sjmisc*have been moved to package*sjlabelled*.

`boot_est()`

to return the estimate from bootstrap replicates.

- The
`print()`

-method for`svyglm.nb()`

-objects now also prints confidence intervals.

`se()`

did not work for`icc()`

-objects, when the mixed model had more than one random effect term.

`cv_error()`

and`cv_compare()`

to compute the root mean squared error for test and training data from cross-validation.`props()`

to calculate proportions in a vector, supporting multiple logical statements.`or_to_rr()`

to convert odds ratio estimates into risk ratio estimates.`mn()`

,`md()`

and`sm()`

to calculate mean, median or sum of a vector, but using`na.rm = TRUE`

as default.- S3-generics for
`svyglm.nb`

-models:`family()`

,`print()`

,`formula()`

,`model.frame()`

and`predict()`

.

- Fixed error in computation of
`mse()`

.

- Functions
`std()`

and`center()`

were removed and are now in the sjmisc-package.

`svyglm.nb()`

to compute survey-weighted negative binomial regressions.`xtab_statistics()`

to compute various measures of assiciation for contingency tables.- Added S3-
`model.frame()`

-function for`gee`

-models.

`se()`

gets a`type`

-argument, which applies to generalized linear mixed models. You can now choose to compute either standard errors with delta-method approximation for fixed effects only, or standard errors for joint random and fixed effects.

`prop()`

did not work for non-labelled data frames when used with grouped data frames.

`svy()`

to compute robust standard errors for weighted models, adjusting the residual degrees of freedom to simulate sampling weights.`zero_count()`

to check whether a poisson-model is over- or underfitting zero-counts in the outcome.`pred_accuracy()`

to calculate accuracy of predictions from model fit.`outliers()`

to detect outliers in (generalized) linear models.`heteroskedastic()`

to check linear models for (non-)constant error variance.`autocorrelation()`

to check linear models for auto-correlated residuals.`normality()`

to check whether residuals in linear models are normally distributed or not.`multicollin()`

to check predictors in a model for multicollinearity.`check_assumptions()`

to run a set of model assumption checks.

`prop()`

no longer works within dplyr’s`summarise()`

function. Instead, when now used with grouped data frames, a summary of proportions is directly returned as tibble.`se()`

now computes adjusted standard errors for generalized linear (mixed) models, using the Taylor series-based delta method.

- Package depends on R-version >= 3.3.

`prop()`

gets a`digits`

-argument to round the return value to a specific number of decimal places.

- Largely revised the documentation.

`prop()`

to calculate proportion of values in a vector.`mse()`

to calculate the mean square error for models.`robust()`

to calculate robust standard errors and confidence intervals for regression models, returned as tidy data frame.

`split_half()`

to compute the split-half-reliability of tests or questionnaires.`sd_pop()`

and`var_pop()`

to compute population variance and population standard deviation.

`se()`

now also computes the standard error from estimates (regression coefficients) and p-values.

- Added S3-
`print`

-method for`mwu()`

-function. `get_model_pval()`

to return a tidy data frame (tibble) of model term names, p-values and standard errors from various regression model types.`se_ybar()`

to compute standard error of sample mean for mixed models, considering the effect of clustering on the standard error.`std()`

and`center()`

to standardize and center variables, supporting the pipe-operator.

`se()`

now also computes the standard error for intraclass correlation coefficients, as returned by the`icc()`

-function.`std_beta()`

now always returns a tidy data frame (tibble) with model term names, standardized estimate, standard error and confidence intervals.`r2()`

now also computes alternative omega-squared-statistics, if null model is given.