# sjstats 0.8.0

## New functions

`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.

## Changes to functions

`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.

# sjstats 0.7.1

## General

- Package depends on R-version >= 3.3.

## Changes to functions

`prop()`

gets a `digits`

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

# sjstats 0.7.0

## General

- Largely revised the documentation.

## New functions

`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.

# sjstats 0.6.0

## New functions

`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.

## Changes to functions

`se()`

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

# sjstats 0.5.0

## New functions

- 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.

## Changes to functions

`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.