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`compare_performance()` allows comparisons of AIC values for models with and
without transformed response variables.

* Also, `model_performance()` now corrects both AIC and BIC values for models
with transformed response variables.

### Plotting and printing

* The `print()` method for `binned_residuals()` now prints a short summary of
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* Fixed issue in `check_heteroscedasticity()` for *aov* objects.

* Fixed issues for *lmrob* and *glmrob* objects.

# performance 0.4.4

## General

* Removed `logLik.felm()`, because this method is now implemented in the *lfe*
package.

* Support for `DirichletRegModel` models.

## New functions

* `check_itemscale()` to describe various measures of internal consistencies for
scales which were built from several items from a PCA, using
`parameters::principal_components()`.

* `r2_efron()` to compute Efron's pseudo R2.

## Bug fixes

* Fixed issue in documentation of `performance_score()`.

# performance 0.4.3

## General

* Support for `mixor`, `cpglm` and `cpglmm` models.

## New functions

* `performance_aic()` as a small wrapper that returns the AIC. It is a generic
function that also works for some models that don't have a AIC method (like
Tweedie models).

* `performance_lrt()` as a small wrapper around `anova()` to perform a
Likelihood-Ratio-Test for model comparison.

## Bug fixes

* Fix issues with CRAN checks.

## Changes to functions

* `model_performance()` now calculates AIC for Tweedie models.

# performance 0.4.2

## General

* Support for `bracl`, `brmultinom`, `fixest`, `glmx`, `glmmadmb`, `mclogit`,
`mmclogit`, `vgam` and `vglm` models.

* `model_performance()` now supports *plm* models.

* `r2()` now supports *complmrob* models.

* `compare_performance()` now gets a `plot()`-method (requires package
**see**).

## Changes to functions

* `compare_performance()` gets a `rank`-argument, to rank models according to
their overall model performance.

* `compare_performance()` has a nicer `print()`-method now.

* Verbosity for `compare_performance()` was slightly adjusted.

* `model_performance()`-methods for different objects now also have a
`verbose`-argument.

## Minor changes

* `check_collinearity()` now no longer returns backticks in row- and column
names.

## Bug fixes

* Fixed issue in `r2()` for `wbm`-models with cross-level interactions.

* `plot()`-methods for `check_heteroscedasticity()` and `check_homogeneity()`
now work without requiring to load package *see* before.

* Fixed issues with models of class `rlmerMod`.

# performance 0.4.0

## General

* `performance()` is an alias for `model_performance()`.

## Deprecated and Defunct

* `principal_components()` was removed and re-implemented in the
**parameters**-package. Please use `parameters::principal_components()` now.

## Changes to functions

* `check_outliers()` now also works on data frames.

* Added more methods to `check_outliers()`.

* `performance_score()` now also works on `stan_lmer()` and `stan_glmer()`
objects.

* `check_singularity()` now works with models of class *clmm*.

* `r2()` now works with models of class *clmm*, *bigglm* and *biglm*.

* `check_overdispersion()` for mixed models now checks that model family is
Poisson.

## Bug fixes

* Fixed bug in `compare_performance()` that toggled a warning although models
were fit from same data.

* Fixed bug in `check_model()` for *glmmTMB* models that occurred when checking
for outliers.

# performance 0.3.0

## General

* Many `check_*()`-methods now get a `plot()`-method. Package **see** is
required for plotting.

* `model_performance()` gets a preliminary `print()`-method.

## Breaking changes

* The attribute for the standard error of the Bayesian R2 (`r2_bayes()`) was
renamed from `std.error` to `SE` to be in line with the naming convention of
other easystats-packages.

* `compare_performance()` now shows the Bayes factor when all compared models
are fit from the same data. Previous behaviour was that the BF was shown when
models were of same class.

## Changes to functions

* `model_performance()` now also works for *lavaan*-objects.

* `check_outliers()` gets a `method`-argument to choose the method for detecting
outliers. Furthermore, two new methods (Mahalanobis Distance and Invariant
Coordinate Selection) were implemented.

* `check_model()` now performs more checks for GLM(M)s and other model objects.

* `check_model()` gets a `check`-argument to plot selected checks only.

* `r2_nakagawa()` now returns r-squared for models with singular fit, where no
random effect variances could be computed. The r-squared then does not take
random effect variances into account. This behaviour was changed to be in line
with `MuMIn::r.squaredGLMM()`, which returned a value for models with singular
fit.

* `check_distribution()` now detects negative binomial and zero-inflated
distributions. Furthermore, attempt to improve accuracy.

* `check_distribution()` now also accepts a numeric vector as input.

* `compare_performance()` warns if models were not fit from same data.

## New check-functions

* `check_homogeneity()` to check models for homogeneity of variances.

## Bug fixes

* Fixed issues with `compare_performance()` and row-ordering.

* Fixed issue in `check_collinearity()` for zero-inflated models, where the
zero-inflation component had not enough model terms to calculate
multicollinearity.

* Fixed issue in some `check_*()` and `performance_*()` functions for models
with binary outcome, when outcome variable was a factor.

# performance 0.2.0

## General

* `r2()` now works for more regression models.

* `r2_bayes()` now works for multivariate response models.

* `model_performance()` now works for more regression models, and also includes
the log-loss, proper scoring rules and percentage of correct predictions as
new metric for models with binary outcome.

## New performance-functions

* `performance_accuracy()`, which calculates the predictive accuracy of linear
or logistic regression models.

* `performance_logloss()` to compute the log-loss of models with binary outcome.
The log-loss is a proper scoring function comparable to the `rmse()`.

* `performance_score()` to compute the logarithmic, quadratic and spherical
proper scoring rules.

* `performance_pcp()` to calculate the percentage of correct predictions for
models with binary outcome.

* `performance_roc()`, to calculate ROC-curves.

* `performance_aicc()`, to calculate the second-order AIC (AICc).

## New check-functions

* `check_collinearity()` to calculate the variance inflation factor and check
model predictors for multicollinearity.

* `check_outliers()` to check models for influential observations.

* `check_heteroscedasticity()` to check models for (non-)constant error
variance.

* `check_normality()` to check models for (non-)normality of residuals.

* `check_autocorrelation()` to check models for auto-correlated residuals.

* `check_distribution()` to classify the distribution of a model-family using
machine learning.

## New indices-functions

* `r2_mckelvey()` to compute McKelvey and Zavoinas R2 value.

* `r2_zeroinflated()` to compute R2 for zero-inflated (non-mixed) models.

* `r2_xu()` as a crude R2 measure for linear (mixed) models.

## Breaking changes

* `model_performance.stanreg()` and `model_performance.brmsfit()` now only
return one R2-value and its standard error, instead of different (robust) R2
measures and credible intervals.

* `error_rate()` is now integrated in the `performance_pcp()`-function.

## Changes to functions

* `model_performance.stanreg()` and `model_performance.brmsfit()` now also
return the _WAIC_ (widely applicable information criterion).

* `r2_nakagawa()` now calculates the full R2 for mixed models with
zero-inflation.

* `icc()` now returns `NULL` and no longer stops when no mixed model is
provided.

* `compare_performance()` now shows the Bayes factor when all compared models
are of same class.

* Some functions get a `verbose`-argument to show or suppress warnings.

## Bug fixes

* Renamed `r2_coxnell()` to `r2_coxsnell()`.

* Fix issues in `r2_bayes()` and `model_performance()` for ordinal models resp.
models with cumulative link (#48).

* `compare_performance()` did not sort the `name`-column properly, if the
columns `class` and `name` were not in the same alphabetical order (#51).

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