- Package
*insight*since version 0.9.5 now returns the “raw” (untransformed, i.e. original) data that was used to fit the model also for log-transformed variables. Thus, exponentiation like using`terms = "predictor [exp]"`

is no longer necessary.

`mlogit`

(package**mlogit**)

`plot()`

now can also create partial residuals plots. There, arguments`residuals`

,`residuals.type`

and`residuals.line`

were added to add partial residuals, the type of residuals and a possible loess-fit regression line for the residual data.

- The message for models with a back-transformation to the response scale (all non-Gaussian models), that standard errors are still on the link-scale, did not show up for models of class
`glm`

since some time. Should be fixed now. - Fixed issue with
`ggpredict()`

and`rlmerMods`

models when using factors as adjusted terms. - Fixed issue with brms-multi-response models.

`mclogit`

(package**mclogit**)

- Fixed issues due to latest
*rstanarm*update. - Fixed some issues around categorical/cumulative
*brms*models when the outcome is numeric. - Fixed bug with factor level ordering when plotting raw data from
`ggeffect()`

.

`ggpredict()`

gets a new`type`

-option,`"zi.prob"`

, to predict the zero-inflation probability (for models from*pscl*,*glmmTMB*and*GLMMadaptive*).- When model has log-transformed response variable and
`add.data = TRUE`

in`plot()`

, the raw data points are also transformed accordingly. `plot()`

with`add.data = TRUE`

first adds the layer with raw data, then the points / lines for the marginal effects, so raw data points to not overlay the predicted values.- The
`terms`

-argument now also accepts the name of a variable to define specific values. See vignette*Marginal Effects at Specific Values*.

- Fix issues in cluster-robust variance-covariance estimation when
`vcov.type`

was not specified.

- Fixed issues to due changes in other CRAN packages.

*ggeffects*now requires*glmmTMB*version 1.0.0 or higher.- Added human-readable alias-options to the
`type`

-argument.

- Fixed issue when log-transformed predictors where held constant and their typical value was negative.
- Fixed issue when plotting raw data to a plot with categorical predictor in the x-axis, which had numeric factor levels that did not start at
`1`

. - Fixed issues for model objects that used (log) transformed
`offset()`

terms.

- Reduce package dependencies.
- New package-vignette
*(Cluster) Robust Standard Errors*.

`mixor`

(package**mixor**),`cgam`

,`cgamm`

(package**cgam**)

- Fix CRAN check issues due to latest
*emmeans*update.

- The argument
`x.as.factor`

is considered as less useful and was removed.

`fixest`

(package**fixest**),`glmx`

(package**glmx**).

- Reduce package dependencies.
`plot(rawdata = TRUE)`

now also works for objects from`ggemmeans()`

.`ggpredict()`

now computes confidence intervals for predictions from`geeglm`

models.- For
*brmsfit*models with`trials()`

as response variable,`ggpredict()`

used to choose the median value of trials were the response was hold constant. Now, you can use the`condition`

-argument to hold the number of trials constant at different values. - Improve
`print()`

.

- Fixed issue with
`clmm`

-models, when group factor in random effects was numeric. - Raw data is no longer omitted in plots when grouping variable is continuous and added raw data doesn’t numerically match the grouping levels (e.g., mean +/- one standard deviation).
- Fix CRAN check issues due to latest
*geepack*update.

- The use of
`emm()`

is discouraged, and so it was removed.

`bracl`

,`brmultinom`

(package**brglm2**) and models from packages**bamlss**and**R2BayesX**.

- Updated package dependencies.
`plot()`

now uses dodge-position for raw data for categorical x-axis, to align raw data points with points and error bars geoms from predictions.- Updated and re-arranged internal color palette, especially to have a better behaviour when selecting colors from continuous palettes (see
`show_pals()`

).

- Added a
`vcov()`

function to calculate variance-covariance matrix for marginal effects.

`ggemmeans()`

now also accepts`type = "re"`

and`type = "re.zi"`

, to add random effects variances to prediction intervals for mixed models.- The ellipses-argument
`...`

is now passed down to the`predict()`

-method for*gamlss*-objects, so predictions can be computed for sigma, nu and tau as well.

- Fixed issue with wrong order of plot x-axis for
`ggeffect()`

, when one term was a character vector.

- The use of
`ggaverage()`

is discouraged, and so it was removed. - The name
`rprs_values()`

is now deprecated, the function is named`values_at()`

, and its alias is`representative_values()`

. - The
`x.as.factor`

-argument defaults to`TRUE`

.

`ggpredict()`

now supports cumulative link and ordinal*vglm*models from package**VGAM**.- More informative error message for
*clmm*-models when`terms`

included random effects. `add.data`

is an alias for the`rawdata`

-argument in`plot()`

.`ggpredict()`

and`ggemmeans()`

now also support predictions for*gam*models from`ziplss`

family.

- Improved
`print()`

-method for ordinal or cumulative link models. - The
`plot()`

-method no longer changes the order of factor levels for groups and facets. `pretty_data()`

gets a`length()`

argument to define the length of intervals to be returned.

- Added “population level” to output from print-method for
*lme*objects. - Fixed issue with correct identification of gamm/gamm4 models.
- Fixed issue with weighted regression models from
*brms*. - Fixed broken tests due to changes of forthcoming
*effects*update.

- Revised docs and vignettes - the use of the term
*average marginal effects*was replaced by a less misleading wording, since the functions of**ggeffects**calculate marginal effects at the mean or at representative values, but not average marginal effects. - Replace references to internal vignettes in docstrings to website-vignettes, so links on website are no longer broken.
`values_at()`

is an alias for`rprs_values()`

.

`betabin`

,`negbin`

(package**aod**),`wbm`

(package*panelr*)

`ggpredict()`

now supports prediction intervals for models from*MCMCglmm*.`ggpredict()`

gets a`back.transform`

-argument, to tranform predicted values from log-transformed responses back to their original scale (the default behaviour), or to allow predictions to remain on log-scale (new).`ggpredict()`

and`ggemmeans()`

now can calculate marginal effects for specific values from up to three terms (i.e.`terms`

can be of lenght four now).- The
`ci.style`

-argument from`plot()`

now also applies to error bars for categorical variables on the x-axis.

- Fixed issue with
*glmmTMB*models that included model weights.

- Better support, including confidence intervals, for some of the already supported model types.
- New package-vignette
*Logistic Mixed Effects Model with Interaction Term*.

`gamlss`

,`geeglm`

(package**geepack**),`lmrob`

and`glmrob`

(package**robustbase**),`ols`

(package**rms**),`rlmer`

(package**robustlmm**),`rq`

and`rqss`

(package**quantreg**),`tobit`

(package**AER**),`survreg`

(package**survival**)

- The steps for specifying a range of values (e.g.
`terms = "predictor [1:10]"`

) can now be changed with`by`

, e.g.`terms = "predictor [1:10 by=.5]"`

(see also vignette*Marginal Effects at Specific Values*). - Robust standard errors for predictions (see argument
`vcov.fun`

in`ggpredict()`

) now also works for following model-objects:`coxph`

,`plm`

,`polr`

(and probably also`lme`

and`gls`

, not tested yet). `ggpredict()`

gets an`interval`

-argument, to compute prediction intervals instead of confidence intervals.`plot.ggeffects()`

now allows different horizontal and vertical jittering for`rawdata`

when`jitter`

is a numeric vector of length two.

- Models with
`AsIs`

-conversion from division of two variables as dependent variable, e.g.`I(amount/frequency)`

, now should work. `ggpredict()`

failed for`MixMod`

-objects when`ci.lvl=NA`

.

- Minor revisions to docs and vignettes.
- Reduce package dependencies.
- Better support, including confidence intervals, for some of the already supported model types.
- New package-vignette
*Customize Plot Appearance*.

`ggemmeans()`

now supports`type = "fe.zi"`

for**glmmTMB**-models, i.e. predicted values are conditioned on the fixed effects and the zero-inflation components of glmmTMB-models.`ggpredict()`

now supports**MCMCglmm**,**ivreg**and**MixMod**(package**GLMMadaptive**) models.`ggemmeans()`

now supports**MCMCglmm**and**MixMod**(package**GLMMadaptive**) models.`ggpredict()`

now computes confidence intervals for**gam**models (package**gam**).

`new_data()`

, to create a data frame from all combinations of predictor values. This data frame typically can be used for the`newdata`

-argument in`predict()`

, in case it is necessary to quickly create an own data frame for this argument.

`ggpredict()`

no longer stops when predicted values with confidence intervals for**glmmTMB**- and other zero-inflated models can’t be computed with`type = "fe.zi"`

, and only returns the predicted values without confidence intervals.- When
`ggpredict()`

fails to compute confidence intervals, a more informative error message is given. `plot()`

gets a`connect.lines`

-argument, to connect dots from plots with discrete x-axis.

`ggpredict()`

did not work with**glmmTMB**- and other zero-inflated models, when`type = "fe.zi"`

and model- or zero-inflation formula had a polynomial term that was held constant (i.e. not part of the`terms`

-argument).- Confidence intervals for zero-inflated models and
`type = "fe.zi"`

could not be computed when the model contained polynomial terms and a*very*long formula (issue with`deparse()`

, cutting off very long formulas). - The
`plot()`

-method put different spacing between groups when a numeric factor was used along the x-axis, where the factor levels where non equal-spaced. - Minor fixes regarding calculation of predictions from some already supported models
- Fixed issues with multiple response models of class
`lm`

in`ggeffects()`

. - Fixed issues with encoding in help-files.

- Minor changes to meet forthcoming changes in purrr.
- For consistency reasons, both
`type = "fe"`

and`type = "re"`

return population-level predictions for mixed effects models (**lme4**,**glmmTMB**). The difference is that`type = "re"`

also takes the random effect variances for prediction intervals into account. Predicted values at specific levels of random effect terms is described in the package-vignettes*Marginal Effects for Random Effects Models*and*Marginal Effects at Specific Values*. - Revised docs and vignettes.
- Give more informative warning for misspelled variable names in
`terms`

-argument. - Added custom (pre-defined) color-palettes, that can be used with
`plot()`

. Use`show_pals()`

to show all available palettes. - Use more appropriate calculation for confidence intervals of predictions for model with zero-inflation component.

`ggpredict()`

and`ggeffect()`

now support**brms**-models with additional response information (like`trial()`

).`ggpredict()`

now supports**Gam**,**glmmPQL**,**clmm**, and**zerotrunc**-models.- All models supported by the
**emmeans**should also work with the new`ggemmeans()`

-function. Since this function is quite new, there still might be some bugs, though.

`ggemmeans()`

to compute marginal effects by calling`emmeans::emmeans()`

.`theme_ggeffects()`

, which can be used with`ggplot2::theme_set()`

to set the**ggeffects**-theme as default plotting theme. This makes it easier to add further theme-modifications like`sjPlot::legend_style()`

or`sjPlot::font_size()`

.

- Added prediction-type based on simulations (
`type = "sim"`

) to`ggpredict()`

, currently for models of class**glmmTMB**and**merMod**. `x.cat`

is a new alias for the argument`x.as.factor`

.- The
`plot()`

-method gets a`ci.style`

-argument, to define different styles for the confidence bands for numeric x-axis-terms. - The
`print()`

-method gets a`x.lab`

-argument to print value labels instead of numeric values if`x`

is categorical. `emm()`

now also supports all prediction-types, like`ggpredict()`

.

- Fixed issue where confidence intervals could not be computed for variables with very small values, that differ only after the second decimal part.
- Fixed issue with
`ggeffect()`

, which did not work if data had variables with more that 8 digits (fractional part longer than 8 numbers). - Fixed issue with multivariate response models fitted with
**brms**or**rstanarm**when argument`ppd = TRUE`

. - Fixed issue with glmmTMB-models for
`type = "fe.zi"`

, which could mess up the correct order of predicted values for`x`

. - Fixed minor issue with glmmTMB-models for
`type = "fe.zi"`

or`type = "re.zi"`

, when first terms had the`[all]`

-tag. - Fixed minor issue in the
`print()`

-method for mixed effects models, when predictions were conditioned on all model terms and adjustment was only done for random effects (output-line “adjusted for”). - Fixed issue for mixed models, where confidence intervals were not completely calculated, if
`terms`

included a factor and`contrasts`

were set to other values than`contr.treatment`

. - Fixed issue with messed up order of confidence intervals for
`glm`

-object and heteroskedasticity-consistent covariance matrix estimation. - Fixed issue for
**glmmTMB**-models, when variables in dispersion or zero-inflation formula did not appear in the fixed effects formula. - The
`condition`

-argument was not always considered for some model types when calculating confidence intervals for predicted values.