The deprecated argument

`ppd`

was removed.Some of the deprecated arguments in

`plot()`

were removed.Pooling functions now use the model’s degrees of freedom to calculate the critical values for the confidence intervals.

`test = "slope"`

(or`test = "trend"`

) are aliases in`test_predictions()`

for`test = NULL`

with numeric predictors.`predict_response()`

(and`ggpredict()`

,`ggemmeans()`

and`ggeffect()`

) get an argument`bias_correction`

, to correct for bias when back-transforming predictions for non-Gaussian mixed models.

- Added support for models of class
`glm_weightit`

,`ordinal_weightit`

,`multinom_weightit`

from package*WeightIt*.

Fixed issues for multivariate response models due changes in the last

*insight*package updates.Fixed issue with swapped lower and higher confidence interval values for models with inverse-link.

Fixed CRAN check issues due to breaking changes in the last

*marginaleffects*update.

- The deprecated argument
`ci.lvl`

in`test_predictions()`

was removed.

`ggpredict()`

now supports models of class`glmgee`

(package*glmtoolbox*).`ggemmeans()`

gains arguments`vcov_fun`

,`vcov_type`

and`vcov_args`

to specify the variance-covariance matrix for the marginal means, similar to what is already available in`ggpredict()`

and`ggaverage()`

.When

`test = "contrast"`

, the`engine`

is automatically set to`"emmeans"`

in`test_predictions()`

.`ggaverage()`

(or`predict_response(..., margin = "empirical")`

) now also supports following`type`

options for zero-inflated models:`"zi_prob"`

,`"zero_inflated"`

and`"fixed"`

.Support for zero-inflated models was massively improved in

`test_predictions()`

, which, for instance, now also supports`scale = "zprob"`

to calculate contrasts for the zero-inflation probability for zero-inflated models from*glmmTMB*or*pscl*. Furthermore, when predictions for zero-inflation probabilities were calculated using`pr <- predict_response(..., type = "zi_prob")`

, corresponding contrasts will be calculated with`test_predictions(pr)`

automatically. Additionally, other types for models with zero-inflation component (`"zero_inflated"`

,`"fixed"`

) are supported as well.`ggeffect()`

now warns the user about arguments that are supported by`ggpredict()`

or`ggemmeans()`

, but not by this function (e.g.,`vcov_fun`

).Improved accuracy of standard errors for

`test_predictions(..., engine = "ggeffects")`

.The

`terms`

argument now also accepts the shortcut`"percentile"`

(plus numeric value) to select a range of percentiles for continuous variables, e.g.`terms = "x [percentile90]"`

to select a range of the 90% percentile.

Fixed issue with

*brms*models with monotonic effects in formula (`mo()`

).Fixed issue in

`vcov()`

for`ggeffects`

objects, which could occur in rare situations when some of the predictors were character vectors.Fixed issue with calculation of standard errors when one of the focal term was a character vector.

Fixed issue in

`plot()`

method with`show_data = TRUE`

, where in certain situations the raw data points were not colored when groups were present.Fixed issue in

`plot()`

with too many data points when collapsing random effects groups.

`ggpredict()`

now works for models of class`clm2`

from package*ordinal*, however, confidence intervals are not yet supported for these models.`ggeffect()`

now passes the`latent`

argument for models with ordinal outcome down to`effects::Effect()`

, to plot effects for ordinal models on the latent scale.When argument

`test`

in`test_predictions()`

is`"interaction"`

,`"consecutive"`

, or a data frame,*emmeans*is automatically used as backend, as this is the relevant package that supports these argument types.`format()`

(and hence,`print()`

) for`test_predictions()`

gains a`combine_levels`

argument, to combine levels of the focal term in the output table.The

`engine`

argument in`test_predictions()`

can now also be`"ggeffects"`

. However, this is currently work-in-progress and offers muss less options as the default engine,`"marginaleffects"`

. It can be faster in some cases, though, and works for comparing predicted random effects in mixed models.`test_predictions()`

now automatically falls back to engines`"emmeans"`

or`"ggeffects"`

if the*marginaleffects*(or*emmeans*) package is not installed.`predict_response()`

,`test_predictions()`

and`ggpredict()`

will warn the user when all focal terms are only included as random effects in the model and no appropriate`type`

or`margin`

is specified. This is to avoid meaningless results.`plot()`

gets an`n_rows`

argument, to define the number of rows for the panel alignment. This is useful when the number of facets is large and the default alignment is not optimal.The

`ppd`

argument for Bayesian models will be superseded by the`interval`

argument, i.e.`ppd = TRUE`

is equivalent to`interval = "prediction"`

(and`ppd = FALSE`

is equivalent to`interval = "confidence"`

).When

`back_transform = FALSE`

, and model has a transformed response variable, the`plot()`

method for`ggeffects`

objects now rescales the raw data points. This ensures that the raw data points are plotted on the same scale as the predicted values when`show_data = TRUE`

.Minor revisions of documentation and vignettes, to improve readability and clarity.

Several arguments have been deprecated and replaced by new argument names. A warning is printed when deprecated arguments are used. The deprecated arguments will be removed in a future release.

Fixed issue in

`print()`

for`ggeffect()`

and models with ordinal outcome, where one column was too much in the output.Fixed issue in

`test_predictions()`

with wrong order of term labels when a focal term was a character vector.Fixed issue in

`ggpredict()`

with`wbm`

models from package*panelr*.Fixed issue in

`ggemmeans()`

for`glmmTMB`

models with zero-inflation, when`terms`

included variables that were specified in the conditional, but not in the zero-inflation model formula.Fixed issue in

`ggpredict()`

for Stan models (from packages*rstanarm*and*brms*) where the`ci_level`

argument was not correctly recognized.Fixed CRAN check issues due to latest

*marginaleffects*update.

`ggemmeans()`

(and hence,`predict_response(..., margin = "marginalmeans"))`

now supports`type = "zi_prob"`

for zero-inflated models from package*glmmTMB*, i.e. can now predict the zero-inflation probability.`test_predictions()`

and`ggaverage()`

were updated to work with the latest release of the*marginaleffects*package. That release fixed issues with inaccurate standard errors for*glmmTMB*models.`test_predictions()`

gains a`margin`

argument, to indicate how to marginalize over non-focal terms. This ensures that estimates of pairwise comparisons are in line with estimates of predictions.`test_predictions()`

gains an`engine`

argument, to indicate which package to use to compute pairwise comparisons or contrasts. By default, the*marginaleffects*package is used, but you can also use the*emmeans*package.

- Fixed issue in
`ggeffect()`

when representative values for a focal term included a zero, e.g.`terms = "focal [0,3,5]"`

.

Overhaul of the documentation (again), to provide more clarity about the terminology “adjusted predictions”, “marginal means” and “marginal effects”, and how to calculate each of these quantities using the

*ggeffects*package.`print_html()`

methods were updated to work with the latest release of*tinytable*.New

`print_md()`

method, to print the output as markdown table. This is useful inside RMarkdown or Quarto documents, where the output can be directly included.

`predict_response()`

as “generic” high-level function, which is a replacement for`ggpredict()`

,`ggemmeans()`

and`ggaverage()`

. The new function is more clear about how the function marginalizes over non-focal terms. The`margin`

argument can be used to specify how to marginalize over non-focal terms, i.e. which function internally is used to compute the marginal effects.

The documentation was revised, to provide more clarity about what the package functions do and how to decide, which function or method to calculate marginal effects is the most appropriate.

Improved calculation of prediction intervals for Poisson regression models.

Improved handling of the

`vcov_fun`

argument. This argument now accepts an estimation type as string, e.g.`vcov_fun = "HC0"`

, which is then used to compute the variance-covariance matrix. Thus, it is no longer necessary to define both`vcov_fun`

and`vcov_type`

, if the variance-covariance matrix is covered by one of the pre-defined estimation types. See`?ggpredict`

for details.`hypothesis_test()`

now also accepts the`vcov_fun`

argument, and not only`vcov`

. This ensures consistency with the`vcov_fun`

argument in`ggpredict()`

. Furthermore, the information about the type of variance-covariance matrix is saved to the*ggeffects*object returned by`ggpredict()`

,`predict_response()`

etc., and if this information is available, it is automatically used in`hypothesis_test()`

when a*ggeffects*object is passed to the function.

Fixed bug in wrong order of printed (sub-)tables for predictions.

Fixed wrong table column name for confidence interval columns for other confidence levels than the default 95% in

`print()`

for`ggeffects`

objects.Fixed issue with

`ggpredict()`

for models of class`fixest`

when the cluster variable was numeric.

- The
`print()`

method has been revised. A`format()`

method was added, which allows to format the output of`ggpredict()`

(and`ggeffect()`

etc.) for printing. The refactoring of the`print()`

method makes the code base easier to maintain and it is easier to enhance the print-functionality. Now it is possible to create HTML tables as well, using`print_html()`

. The style of the output has also slightly changed. By default, confidence intervals are no longer enclosed in parentheses. You can change this behaviour by passing the`ci_brackets`

argument to`print()`

(see examples), or permanently define custom parentheses or brackets with, e.g.,`options(ggeffects_ci_brackets = c("[", "]"))`

. Additionally, there are new arguments to further control the output of the tables:`collapse_ci`

can be used to collapse confidence intervals into a single column together with the predicted values.`collapse_tables`

can be used to collapse multiple tables into a single table (only applies when there is more than one focal term). Again, these settings can be permanently defined via options (see`?print.ggeffects`

for details).

`print_html()`

, to print the output as HTML table. This method is available for objects from`ggpredict()`

(and alike) as well as`hypothesis_test()`

.

- A new vignette was added, showing examples for the new print-functionality.

- Fixed issue with
`ggpredict()`

for models of class`vglm`

with multivariate responses.

`ggpredict()`

now supports models of class`rqs`

from package*quantreg*.Fixed issues to be compatible with forthcoming update of

*emmeans*.

`ggaverage()`

, to compute average predicted values. This function is based on`marginaleffects::avg_predictons()`

.`pool_comparisons()`

, to pool results from multiple calls to`hypothesis_test()`

, e.g. with imputed data sets.

Support for

`sdmTMB`

(*sdmTMB*) models.Improved support for the

*logistf*package, including models`flic()`

and`flac()`

.Confidence intervals for predictions from

`merMod`

models (package*lme4*) now use the standard errors returned by`predict(..., se.fit = TRUE)`

. This should not affect numerical results, but*can*be more robust for certain edge cases. Note that standard errors are only based on`predict()`

when`tpye = "fixed"`

. For`type = "random"`

, standard errors are still based on the model’s variance-covariance matrix, taking uncertainty from random effects into account.`hypothesis_test()`

now suppports models from package*parsnip*.`johnson_neyman()`

gains a`p_adjust`

argument, to adjust p-values for multiple comparisons. Currently, only`p_adjust = "esarey"`

(resp.`p_adjust = "es"`

) and`p_adjust = "fdr"`

(resp.`p_adjust = "bh"`

) are supported.

`ggpredict()`

now computes appropriate predicted probabilites for models of class`rms::lrm()`

with ordinal outcome.Fixed issue in

`ggpredict()`

for`type = "random"`

when sampling from random effects levels, where the levels were numeric characters with a pattern like`"001"`

,`"002"`

, etc.Fixed minor issue in

`plot.ggalleffects()`

.`...`

arguments in`ggpredict()`

are now passed down to the`predict()`

method for`mgcv::gam()`

models.

- Some function arguments will be renamed, to achieve consistency across the package and across other packages where I’m involved in the development. This will be a soft transition, i.e. the old argument names will still work for some package updates.

The

`typical`

argument now supports a mix of functions for different variable types at which numeric or categorical covariates (non-focal terms) are held constant.Clarification of how the

`re.form`

argument is set when using`type = "random"`

resp.`type = "fixed"`

in`ggpredict()`

.`hypothesis_test()`

now returns the standard error of contrasts or pairwise comparisons as attribute`standard_error`

. This can be used to compute the test-statistic, if required. In forthcoming updates, there will be methods for`insight::get_statistic()`

and`parameters::model_parameters()`

to include standard errors and test-statistics in the output.`test_predictions()`

was added as an alias for`hypothesis_test()`

.

- Fixed issue in
`hypothesis_test()`

for mixed models, which sometimes failed when random effects group variables were numeric, and not factors.

`johnson_neyman()`

, to create Johnson-Neyman intervals and plots from`ggeffects`

objects.

Better automatic handling of offset-terms, both for predictions and generating plots with raw data. When the model formula contains an offset-term, and the offset term is fixed at a specific value, the response variable is now automatically transformed back to the original scale, and the offset-term is added to the predicted values. A warning is printed when model contains transformed offset-terms that are not fixed, e.g. via the

`condition`

argument.`ggeffect()`

now supports`nestedLogit`

models.

Fixed issue in

`hypothesis_test()`

, where the`by`

argument did not work together with the`collapse_levels`

argument.Fixed issue in

`plot()`

method when adding raw data points for data frame that had now row names.

- To avoid confusion when adding raw data or residuals to plots, the
`jitter`

argument that is used to add some noice to data points to avoid overlapping now defaults to`NULL`

. Formerly, a small jitter was added by default, leading to confusion when data points did not match the original data.

The

`plot()`

method gets a`label.data`

argument, to add row names to data points when`add.data = TRUE`

.`tibbles`

are always converted into data frames, to avoid issues.`hypothesis_test()`

gains a`by`

argument, to specify a variable that is used to group the comparisons or contrasts. This is useful for models with interaction terms.

Plotting residuals did not work when model object passed to

`ggpredict()`

were inside a list, or when called from inside functions (scoping issues).Fixed issue where plotting raw data (i.e.

`plot(..., add.data = TRUE)`

) did not work when there were missing data in weight variables (i.e. when the regression model used weights).Fixes issue in

`plot()`

when no term was specified in the call to`ggpredict()`

.Fixed issues with robust estimation for models of package

*pscl*.Fixed issues introduced by breaking changes in

*marginaleffects*.

Support for

`nestedLogit`

(*nestedLogit*) models.`hyothesis_test()`

gains a`scale`

argument, to explicitely modulate the scale of the contrasts or comparisons (e.g.`"response"`

or`"link"`

, or`"exp"`

to return transformed contrasts/comparisons).`hyothesis_test()`

now includes the response level for models with ordinal outcomes (and alike).When

`ggpredict()`

is used inside functions and a name for a vector variable (passed as argument to that function) in`terms`

is used, the variable is now correctly recognized.Partial residuals (when

`plot(..., residuals = TRUE)`

) now supports more linear (mixed) models, including models from package*lme*(such as`gls()`

or`lme()`

).For mixed models,

`type = "random"`

used to calculate*prediction intervals*that always accounted for random effects variances, leading to larger intervals. Using`interval = "confidence"`

together with`type = "random"`

now allows to calculate “usual” confidence intervals for random effects. This is usefule for predictions at specific group levels of random effects (when focal terms are only fixed effects, use`type = "fixed"`

for regular confidence intervals).The

`vcov.fun`

argument can now also be a function that returns a variance-covariance matrix.The

`verbose`

argument in`ggpredict()`

and`hypothesis_test()`

now also toggle messages for the respective`print()`

methods.The

`print()`

method for`hypothesis_test()`

has been revised and now provides more details for possible transformation of the scale of comparisons and contrasts.The

`print()`

method now shows all rows by default when the focal term is a factor. If rows are not shown in the output, a message is printed to inform the user about truncated output.A new vignette about using

*ggeffects*in the context of an intersectional multilevel analysis of individual heterogeneity, using the MAIHDA framework.

Fixed issue with wrong order of x-axis-labels for plots when the focal term on the x-axis was a character vector, where alphabetical order of values did not match order of predictions.

Fixed issues in

`hyothesis_test()`

for models with ordinal outcomes (and alike).

Added a new

`[.ggeffects`

function, which allows to subset`ggeffects`

objects in the same way as regular data frames, i.e. it is now possible to do:`gge <- ggpredict(model, "x1") gge[c(1:2)]`

Using a name for a vector variable in

`terms`

now works from inside functions. E.g., you can now do:`foo <- function(data) { fit <- lm(barthtot ~ c12hour + c172code, data = data) v <- c(20, 50, 70) ggpredict(fit, terms = "c12hour [v]") } foo(efc)`

The

`colors`

argument in`plot()`

can now also be applied to single-colored plots.`hyothesis_test()`

gains a`collapse_level`

argument, to collapse term labels that refer to the same levels into a singel unique level string.

Fixed issue with misplaced residuals when x-axis was categorical and the factor levels were not in alphabetical order.

`pool_predictions()`

now correctly handles models with transformed response variables (like`log(y)`

) and returns the correct back-transformed pooled predictions (and their confidence intervals).Fixed issue with wrong computation of confidence intervals for models of class

`clm`

from package*ordinal*.Fixed failing tests due to changes in the

*logistf*package, which now also supports*emmeans*. That means,`ggemmeans()`

now also works for models from package*logistf*.Fixed bug in

`plot()`

when partial residuals were added (i.e.`residuals = TRUE`

) and`collapse.group`

was provided (in case of mixed models).Fixed issue with on-the-fly created factors inside formulas, which were not correctly treated as factors in the

`plot()`

method. This bug was related to recent changes in`insight::get_data()`

.Fixed issue with wrong labels in

`hyothesis_test()`

for comparisons with many rows, when betas starting with same digit were specified (e.g.`test = "(b1-b13)=(b3-b15)"`

).Fixed issue in

`hyothesis_test()`

for mixed models when focal terms included factors with factor levels that contained a comma.Fixed issue with missing confidence intervals for mixed models when one of the variable names contains white space characters (e.g.

`y ~ 'x a' + xb`

).

- Support for
`mblogit`

(*mclogit*),`phylolm`

and`phyloglm`

(*phylolm*) models.

`hypothesis_test()`

gains an`equivalence`

argument, to compute tests of practical equivalence for contrasts and comparisons.The message whether contrasts or comparisons from

`hypothesis_test()`

are on the link-scale is now printed below the table.Dot arguments (

`...`

) in`hypothesis_test()`

are now passed to the functions in*marginaleffects*, thereby allowing to use further options in functions`marginaleffects::predictions()`

, like`transform`

etc.

- Fixed issues in
`hypothesis_test()`

for mixed models with one focal term only, and when this term was categorical.

- Confidence intervals of adjusted predictions now take the model’s degrees of freedom into account, thereby leading to slightly larger intervals for models that do not have infinite degrees of freedom (like linear models with t-statistic).

`hypothesis_test()`

, to compute contrasts and comparisons of predictions and test differences for statistical significance. Additionally, an accompanying vignette that explains the new function in detail is added.`install_latest()`

, to install the latest official package version from CRAN, or the latest development version from r-universe.An

`as.data.frame()`

method was added, which converts`ggeffects`

objects returned by`ggpredict()`

into data frame, where standard column names are replaced by their related variable names.

Response values are now also back-transformed when these were transformed using

`log2()`

,`log10()`

or`log1p()`

.The

`terms`

argument can now also be a named list. Thus, instead of`terms = c("score [30,50,70]", "status [low, middle]")`

one could also write`terms = list(score = c(30,50,70), status = c("low", "middle"))`

.

Minor changes to meet forthcoming update of

*insight*.`ggpredict()`

or`ggemmeans()`

get a`verbose`

argument to suppress some messages and warnings when calling

- Reduced package dependencies. Packages
*sjlabelled*and*MASS*were moved from imports to suggests.*ggeffects*is now a very lightweight package to compute adjusted predictions and estimated marginal means.

`logitr`

(package**logitr**)

Fixed issue with wrong standard errors for predicting random effect groups for more multiple levels.

Fixed issue in

`ggemmeans()`

, which did not correctly averaged over character vectors when these were hold constant.Fixed bug for models of class

`lme`

when`type = "re"`

was requested.

Fix wrong computations of predictions for

`arm::bayesglm()`

models.Fix CRAN check issues.

Speed improvement for some models when calculating uncertainty intervals of predictions.

Minor fixes.

- Add more informative error message for
*brmsfit*models using`mo()`

with numeric predictors, which only allow to predict for values that are actually present in the data.

Fixed issue with adding raw data points for plots from logistic regression models, when the response variable was no factor with numeric levels.

Fixed issues with CRAN checks.

`orm`

(package**rms**)

Prediction intervals (where possible, or when

`type = "random"`

), are now always based on sigma^2 (i.e.`insight::get_sigma(model)^2`

). This is in line with`interval = "prediction"`

for*lm*, or for predictions based on simulations (when`type = "simulate"`

).`print()`

now uses the name of the focal variable as column name (instead) of`"x"`

).

`collapse_by_group()`

, to generate a data frame where the response value of the raw data is averaged over the levels of a (random effect) grouping factor.

A new vignette was added related to the definition and meaning of “marginal effects” and “adjusted predictions”. To be more strict and to avoid confusion with the term “marginal effect”, which meaning may vary across fields, either “marginal effects” was replaced by “adjusted predictions”, or “adjusted predictions” was added as term throughout the package’s documentation and vignettes.

Allow confidence intervals when predictions are conditioned on random effect groups (i.e. when

`type = "random"`

and`terms`

includes a random effect group factor).Predicted response values based on

`simulate()`

(i.e. when`type = "simulate"`

) is now possible for more model classes (see`?ggpredict`

).`ggpredict()`

now computes confidence intervals for some edge cases where it previously failed (e.g. some models that do not compute standard errors for predictions, and where a factor was included in the model and not the focal term).`plot()`

gains a`collapse.group`

argument, which - in conjunction with`add.data`

- averages (“collapses”) the raw data by the levels of the group factors (random effects).`data_grid()`

was added as more common alias for`new_data()`

.

`ggpredict()`

and`plot()`

for survival-models now always start with time = 1.Fixed issue in

`print()`

for survival-models.Fixed issue with

`type = "simulate"`

for`glmmTMB`

models.Fixed issue with

`gamlss`

models that had`random()`

function in the model formula.Fixed issue with incorrect back-transformation of predictions for

`geeglm`

models.

`residuals.type`

argument in`plot()`

is deprecated. Always using`"working"`

residuals.

`pretty_range()`

and`values_at()`

can now also be used as function factories.`plot()`

gains a`limit.range`

argument, to limit the range of the prediction bands to the range of the data.

Fixed issue with unnecessary back-transformation of log-transformed offset-terms from

*glmmTMB*models.Fixed issues with plotting raw data when predictor on x-axis was a character vector.

Fixed issues from CRAN checks.

- Fixed CRAN check issues.
- Added argument
`interval`

to`ggemmeans()`

, to either compute confidence or prediction intervals.

`averaging`

(package**MuMIn**)

`pool_predictions()`

, to pool multiple`ggeffects`

objects. This can be used when predicted values or estimated marginal means are calculated for models fit to multiple imputed datasets.

- The function
`residualize_over_grid()`

is now exported. - The back-transformation of the response-variable (if these were log-
or square root-transformed in the model) now also works with square
root-transformations and correctly handles
`log1p()`

and`log(mu + x)`

. - Since standard errors were on the link-scale and not back-transformed for non-Gaussian models, these are now no longer printed (to avoid confusion between standard errors on the link-scale and predictions and confidence intervals on the response-scale).

- Fixed issue for mixed models when predictions should be conditioned
on random effects variances (e.g.
`type = "random"`

or`"zi_random"`

), but random effects variances could not be calculated or were almost zero. - Fixed issue with confidence intervals for
`multinom`

models in`ggemmeans()`

. - Fixed issue in
`ggemmeans()`

for models from*nlme*. - Fixed issue with
`plot()`

for some models in`ggeffect()`

. - Fixed issue with computation of confidence intervals for zero-inflated models with offset-term.

- 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`

.