News

Development

New function:

• specify_hypothesis() returns functions to be used in the hypothesis argument of predictions(), comparisons(), and slopes(). This convenience function can be used to specify complex aggregations and estimands for hypothesis tests (ex: by subgroups or with custom functions).

Minor:

• Do not raise extraneous warning for survey package models when the by argument is not used.

Bugs:

• mlogit predict() method does not play well with data.table. Thanks to @andrewheiss for report #1086.

0.19.0

Breaking changes:

• datagrid() no longer includes the response variable by default when it is not explicitly specified by the user. Use the new response argument to include it.
• datagrid(grid_type="balanced") returns factors ordered by level rather than by order of appearance in the original data.
• Order of some rows in the output may differ when using datagrid(). Necessary to fix issue #1079 (see below).

New modeling packages supported:

• flexsurv: Thanks to @mattwarkentin for code contributions in PR #781. https://cran.r-project.org/web/packages/flexsurv/index.html
• REndo: https://cran.r-project.org/web/packages/REndo/index.html

New:

• wts=TRUE tries to retrieves weights used in a weighted fit such as lm() with the weights argument or a model fitted using the survey package. Thanks to @ngreifer for feature request
• print.marginaleffects() supports style="tinytable", which returns a tinytable object. Call print(avg_slopes(model)) to get a nice printed table in Quarto or Rmarkdown documents, via Typst, LaTeX or HTML. Default print format can be set using: options(marginaleffects_print_style="tinytable")
• hypothesis argument accepts a function which takes a marginaleffects data frame and returns a transformed data frame with term and estimate columns.
• datagrid() gets a response argument (default is FALSE) to control if the response variable is included or excluded from the grid-building process.
• The base::subset() and dplyr::filter() functions can be called with a single argument in newdata to select a subset of rows from the dataset used to fit the model.
  • Ex: avg_comparisons(fit, variables = “treatment”, newdata = subset(treatment == 1))`
• Better warning for unsupported arguments.
• df argument in hypotheses() accepts a vector of length 2 to control degrees of freedom in F tests.
• nlme::lme() objects raise a warning about degrees of freedom. Thanks to and @stefgehrig and @huftis for discussion in Issue #960.

Major bugs:

• Some results could be mislabelled with factor variables used in combination with datagrid() or condition. Thanks to @snhansen for report #1079.
• glmmTMB models now report correct standard errors, and raise a warning that these standard errors only account for uncertainty in fixed effect parameters. Thanks to contributors to Issue #1024 and especially to @bbolker for discussion and solution.

Minor bugs:

• Uninformative error when a custom comparison function returns NA in bayesian models. Thanks to @Sandhu-SS for report #1017.
• datagrid() returns an object with full attributes when by is used. Thanks to @Sandhu-SS for report #1058.
• inferences(method="simulation") with inferences() function. Thanks to @davidarmstrong for report #1054.

0.18.0

This release represents a major step towards 1.0.0. Some functions are renamed and now raise deprecation warnings. After 1.0.0, the API will become much more stable, and any change will have to be very deliberate with much lead time and deprecation delays.

Breaking changes:

• tidy() no longer takes the average of estimates in the original model object. Users who want an aggregate estimate should call the relevant avg_*() function, or use the by argument explicitly. The previous behavior led to unexpected behavior and increased code complexity a lot.
• summary() methods are removed. These have never provided any additional information; they just reprinted the output already available with the standard print method. At least the default summary() for data frames (which is now triggered on marginaleffects object) provides a different view on the content of the object.
• plot_cco(), plot_cme(), and plot_cap() were renamed in version 0.9.0, one year ago. They are now fully removed from the package.

New:

• datagrid(grid_type = "balanced") creates a balanced grid with all unique values of categorical predictors. This can be used with predictions() to compute marginal means as in the emmeans package.
• mvgam package support (multivariate generalized additive models)

Deprecation warnings:

• deltamethod() has been named hypotheses() for a while. We now issue a deprecation warning and it will be removed eventually.
• datagridcf() will eventually be deprecated and removed from the package. We will raise a warning for at least one year before removing the function. Identical results can be obtained with datagrid(..., grid_type="counterfactual")
• marginal_means() will eventually be deprecated and removed from the package. We will raise a warning for at least one year before removing the function. Identical results can be obtained using the predictions() function and the grid_type="balanced" argument of datagrid(). Examples are in the marginal means vignette on the website.

Minor:

• Better warning messages for unsupported matrix columns, enhancing user experience and troubleshooting.
• Various improvements to documentation.
• Typos
• Repository hosts model objects for easier testing.

Bug fixes:

• Error on hypotheses(joint = "string") for comparisons() objects (no result was returned). Thanks to @BorgeJorge for report #981.
• Enhanced support for multi-equation Bayesian models with brms models. Thanks to @winterstat for report #1006.
• Parameter names with spaces could break standard errors. Thanks to @Lefty2021 for report #1005.

0.17.0

Breaking changes:

• The comparisons() now uses “forward contrasts” by default for numeric predictors, instead of “centered contrasts”. This can lead to small numerical differences in non-linear models.
• The variables argument of the comparisons() function no longer accepts numeric vectors unless they are of length 2, specifying the low and high contrast values. This is to avoid ambiguity between the two vector version. Users should supply a data frame or a function instead. This is nearly as easy, and removes ambiguity.

New supported packages:

• dbarts: https://cran.r-project.org/package=dbarts
• mvgam: https://nicholasjclark.github.io/mvgam/ Not available on CRAN yet, but this package maintains its own marginaleffects support function.
• rms::Gls: https://cran.r-project.org/package=rms

Misc:

• comparisons(): The variables argument now accepts functions and data frames for factor, character, and logical variables.
• Deprecation warning for: plot_cap(), plot_cme(), and plot_cco(). These function names will be removed in version 1.0.0.
• options(modelsummary_factory_default=...) is respected in Quarto and Rmarkdown documents.

Bugs:

• wts argument now respected in avg_slopes() for binary variables. Thanks to @trose64 for report #961
• Custom functions in the comparison argument of comparisons() did not supply the correct x vector length for bayesian models when the by argument is used. Thanks to @Sandhu-SS for report #931.
• Add support for two facet variables (through facet_grid) when plotting using condition
• comparisons(): When variables is a vector of length two and newdata has exactly two columns, there was ambiguity between custom vectors and length two vector of contrasts. Bug reported by C. Rainey on Twitter.
• Superfluous warning with fixest::fenegbin.

0.16.0

Machine learning support:

• tidymodels package
• mlr3 package

Misc:

• New vignettes:
  • Inverse Probability Weighting
  • Machine Learning
  • Matching
• Add support for hypotheses() to inferences(). Thanks to @Tristan-Siegfried for code contribution #908.
• Support survival::survreg(). Thanks to Carlisle Rainey for Report #911.
• column_names argument in print.marginaleffects() to suppress the printed column names at the bottom of the printout.
• The function supplied to the comparison argument of the comparisons() function can now operate on x and on newdata directly (e.g., to check the number of observations).
• More informative errors from predict().

Bugs:

• Some gamlss models generated an error related to the what argument. Thanks to @DHLocke for Issue #933

0.15.1

• hypotheses(): The FUN argument handles group columns gracefully.
• Native support for Amelia for multiple imputation.

Documentation:

• New section on “Complex aggregations” in the Hypothesis testing vignette.

Bug fix:

• Results of the predictions() function could be inaccurate when (a) running version 0.15.0, (b) type is NULL or invlink(link), (c) model is glm(), and (d) the hypothesis argument is non-numeric. Thanks to @strengejacke for report #903

0.15.0

New:

• Conformal prediction via inferences()
• hypothesis argument now accepts multiple string formulas.
• The type argument now accepts an explicit invlink(link) value instead of silently back-transforming. Users are no longer pointed to type_dictionary. Instead, they should call their function with a bad type value, and they will obtain a list of valid types. The default type value is printed in the output. This is useful because the default type value is NULL, so the user often does not explicitly decide.
• Allow install with Rcpp 1.0.0 and greater.

Support new models:

• survey::svyolr()

Misc:

• inferences(method="simulation") uses the original point estimate rather than the mean of the simulation distribution. Issue #851.
• Better documentation and error messages for newdata=NULL
• Some performance improvements for predictions() and marginalmeans() (#880, #882, @etiennebacher).

Bug fix:

• newdata="median" returned mean of binary variables. Thanks to @jkhanson1970 for report #896.

0.14.0

Breaking changes:

• Row order of the output changes for some objects. Rows are not sorted alphabetically by term, by, and variables explicitly supplied to datagrid. This can affect hypothesis tests computed using the b1, b2, b3, and other indices.
• New procedure numderiv argument use a different procedure to select the step size used in the finite difference numeric derivative used to compute standard errors: abs(x) * sqrt(.Machine$double.eps). The numerical results may not be exactly identical to previous versions of marginaleffects, but the step size should be adequate in a broader variety of cases. Note that users can use the numderiv argument for more control on numeric differentiation, as documented.
• bife models are no longer supported pending investigation in weird results in the tests. Looking for volunteers write more thorough tests.

New:

• Support: logistf package.
• Support: DCchoice package.
• Support: stats::nls
• hypotheses() can now accept raw data frame, which gives a lot of flexibility for custom contrasts and functions. See the Hypothesis vignette for an example.
• numderiv argument allows users to use finite difference (center or forward) or Richardson’s method to compute the numerical derivatives used in the calculation of standard errors.

Bug fixes:

• inferences() supports the cross argument for comparisons() objects. Thanks to Kirill Solovev for report #856.
• splines::bs() in formulas could produce incorrect results due to weirdness in stats::model.matrix(). Thanks to @chiungming for report #831.
• mgcv with ocat are now supported. Thanks to Lorenzo Fabbri for Issue #844.
• quantreg problem with rowid merge did not affect estimates but did not return the full original data. Issue #829.
• get_modeldata() extracts weights variable when available.
• predictions() is no longer broken in some inferences() calls. Issue #853
• Inaccurate averaging with comparison=differenceavg some models where all predictors are categorical. Thanks to Karl Ove Hufthammer for report #865.

Misc:

• Major refactor to simplify the code base and make maintenance easier.

0.13.0

Breaking change:

• glmmTMB: Standard errors are no longer supported because they may have been erroneous. Follow Issue #810 on Github for developments: https://github.com/vincentarelbundock/marginaleffects/issues/810

New:

• hypothesis argument accepts wildcards: hypothesis = "b*=b1"
• s.value column in all output: Shannon transforms for p values. See Greenland (2019).
• marginal_means supports mira (mice objects).
• comparisons(): The variables arguments now accepts arbitrary numeric vectors of length equal to the number of rows in newdata. This allows users to specify fully custom treatment sizes. In the documentation examples, we show how to estimate the difference for a 1 standard deviation shift in a regressor, where the standard deviation is calculated on a group-wise basis.
• comparisons(): the variables argument now accepts “revpairwise”, “revsequential”, “revreference” for factor and character variables.
• comparisons(): the comparison argument now accept “lift” and “liftavg”.

Performance:

• Computing elasticities for linear models is now up to 30% faster (#787, @etiennebacher).

Bug fixes:

• Better handling of environments when newdata is a function call. Thanks to @jcccf for report #814 and to @capnrefsmmat for the proposed fix using the rlang package.
• Degrees of freedom mismatch for joint hypothesis tests. Thanks to @snhansen for report #789.

0.12.0

Breaking change:

• Row order of output has changed for many calls, especially those using the by argument. This may break hypothesis tests conducted by indexing b1, b2, etc. This was necessary to fix Issue #776. Thanks to @marcora for the report.

New:

• hypotheses(): Joint hypothesis tests (F and Chi-square) with the joint and joint_test arguments.
• vcov.hypotheses method.
• wts is now available in plot_predictions(), plot_comparisons(), and plot_slopes().

Bug:

• Wrong order of rows in bayesian models with by argument. Thanks to @shirdekel for report #782.

0.11.2

• vcov() and coef() methods for marginaleffects objects.
• Strings in wts are accepted with the by argument.
• predictions() and avg_predictions() no longer use an automatic backtransformation for GLM models unless hypothesis is NULL.
• vcov() can be used to retrieve a full variance-covariance matrix from objects produced by comparisons(), slopes(), predictions(), or marginal_means() objects.
• When processing objects obtained using mice multiple imputation, the pooled model using mice::pool is attached to the model attribute of the output. This means that functions like modelsummary::modelsummary() will not erroneously report goodness-of-fit statistics from just a single model and will instead appropriately report the statistics for the pooled model. Thanks to @Tristan-Siegfried for PR #740.
• More informative error messages on some prediction problems. Thanks to @andymilne for Report #751.

Performance:

• inferences() is now up to 17x faster and much more memory-efficient when method is "boot" or "rsample" (#770, #771, @etiennebacher).

Bugs:

• brms models with nl=TRUE and a single predictor generated an error. Thanks to @Tristan-Siegried for Report #759.
• avg_predictions(): Incorrect group-wise averaging when all predictors are categorical, the variables variable is used, and we are averaging with avg_ or the by argument. Thanks to BorgeJorge for report #766.
• Bug when datagrid() when called inside a user-written function. Thanks to @NickCH-K for report #769 and to @capnrefsmmat for the diagnostics.

0.11.1

Breaking change:

• Row orders are now more consistent, but may have changed from previous version. This could affect results from hypothesis with b1, b2, … indexing.

Support new models:

• nlme::lme()
• phylolm::phylolm()
• phylolm::phyloglm()

New:

• Vignette on 2x2 experimental designs. Thanks to Demetri Pananos.
• comparisons() accepts data frames with two numeric columns (“low” and “high”) to specify fully customizable contrasts.
• datagrid() gets a new by argument to create apply grid-making functions within groups.
• plot_*() gain a newdata argument for use with by.

Bug:

• comparisons(comparison = "lnratioavg") ignored wts argument. Thanks to Demetri Pananos for report #737.
• ordinal::clm(): incorrect standard errors when location and scale parameters are the same. Thanks to MrJerryTAO for report #718.
• Incorrect label for “2sd” comparisons. Thanks to Andy Milne for report #720.
• Invalid factor levels in datagrid() means newdata argument gets ignored. Thanks to Josh Errickson for report #721.
• Error in models with only categorical predictors and the by argument. Thanks to Sam Brilleman for report #723.
• Elasticities are now supported for ordinal::clm() models. Thanks to MrJerryTAO for report #729.
• glmmTMB models with zero-inflated components are supported. Thanks to @Helsinki-Ronan and @strengejacke for report #734.

0.11.0

Breaking changes:

• type column is replaced by type attribute.
• predictions() only works with officially supported model types (same list as comparisons() and slopes()).

Renamed arguments (backward compatibility is preserved):

• transform_pre -> comparison
• transform_post -> transform

New:

• p_adjust argument: Adjust p-values for multiple comparisons.
• equivalence argument available everywhere.

Performance:

• Much faster results in avg_*() functions for models with only categorical predictors and many rows of data, using deduplication and weights instead of unit-level estimates.
• Faster predictions in lm() and glm() models using RcppEigen.
• Bayesian models with many rows. Thanks to Etienne Bacher. #694
• Faster predictions, especially with standard errors and large datasets.

Bugs:

• Multiple imputation with mira objects was not pooling all datasets. Thanks to @Generalized for report #711.
• Support for more models with offsets. Thanks to @mariofiorini for report #705.
• Error on predictions() with by and wts. Thanks to Noah Greifer for report #695.
• afex: some models generated errors. Thanks to Daniel Lüdecke for report #696.
• group column name is always forbidden. Thanks to Daniel Lüdecke for report #697.
• Blank graphs in plot_comparisons() with a list in variables.
• type="link" produced an error with some categorical brms models. Thanks to @shirdekel for report #703.
• Error on predictions(variables = ...) for glmmTMB models. Thanks to Daniel Lüdecke for report #707.
• by with user-specified function in comparison and factor predictor did not aggregate correctly. Thanks to @joaotedde for report #715.
• ordinal::clm: Support cum.prob and linear.predictor prediction types. Thanks to @MrJerryTAO for report #717.

0.10.0

Performance:

• 2-4x faster execution for many calls. Thanks to Etienne Bacher.

New models supported:

• MCMCglmm::MCMCglmm
• Rchoice::hetprob
• Rchoice::ivpml
• Multiple imputation using mice and any package which can return a list of imputed data frames (e.g., Amelia, missRanger, etc.)

Plot improvements:

• New by argument to display marginal estimates by subgroup.
• New rug argument to display tick marks in the margins.
• New points argument in plot_predictions() to display a scatter plot.
• New gray argument to plot in grayscale using line types and shapes instead of color.
• The effect argument is renamed to variables in plot_slopes() and plot_comparisons(). This improves consistency with the analogous slopes() and comparisons() functions.
• The plotting vignette was re-written.

Other:

• Support multiple imputation with mice mira objects. The multiple imputation vignette was rewritten.
• The variables_grid argument in marginal_means() is renamed newdata. Backward compatibility is maintained.
• avg_*() returns an informative error when vcov is “satterthwaite” or “kenward-roger”
• “satterthwaite” and “kenward-roger” are now supported when newdata is not NULL
• Informative error when hypothesis includes a b# larger than the available number of estimates.
• avg_predictions(model, variables = "x") computes average counterfactual predictions by subgroups of x
• datagrid() and plot_*() functions are faster in datasets with many extraneous columns.
• In predictions(type = NULL) with glm() and Gam() we first make predictions on the link scale and then backtransform them. Setting type="response" explicitly makes predictions directly on the response scale without backtransformation.
• Standard errors now supported for more glmmTMB models.
• Use the numDeriv package for numeric differentiation in the calculation of delta method standard error. A global option can now be passed to numDeriv::jacobian:
  • options(marginaleffects_numDeriv = list(method = "simple", method.args = list(eps = 1e-6)))
  • options(marginaleffects_numDeriv = list(method = "Richardson", method.args = list(eps = 1e-6)))
  • options(marginaleffects_numDeriv = NULL)
• Print:
  • Print fewer significant digits.
  • print.marginaleffects now prints all columns supplied to newdata
  • Less redundant labels when using hypothesis
• Many improvements to documentation.

Bugfixes:

• Standard errors could be inaccurate in models with non-linear components (and interactions) when some of the coefficients were very small. This was related to the step size used for numerical differentiation for the delta method. Issue #684.
• avg_predictions(by =) did not work when the dataset included a column named term. Issue #683.
• brms models with multivariate outcome collapsed categories in comparisons(). Issue #639.
• hypotheses() now works on lists and in calls to lapply(), purrr::map(), etc. Issue #660.

0.9.0

Breaking changes:

• All functions return an estimate column instead of the function-specific predicted, comparisons, dydx, etc. This change only affects unit-level estimates, and not average estimates, which already used the estimate column name.
• The transform_avg argument in tidy() deprecated. Use transform_post instead.
• plot_*(draw=FALSE) now return the actual variable names supplied to the condition argument, rather than the opaque “condition1”, “condition2”, etc.

New models supported:

• blme package.

New features:

• New functions: avg_predictions(), avg_comparisons(), avg_slopes()
• Equivalence, non-inferiority, and non-superiority tests with the hypotheses() function and equivalence argument.
• New experimental inferences() function: simulation-based inferences and bootstrap using the boot, rsample, and fwb package.
• New df argument to set degrees of freedom manually for p and CI.
• Pretty print() for all objects.
• by argument
  • TRUE returns average (marginal) predictions, comparisons, or slopes.
  • Supports bayesian models.
• hypothesis argument
  • Numeric value sets the null used in calculating Z and p.
  • Example: comparisons(mod, transform_pre = "ratio", hypothesis = 1)
• All arguments from the main functions are now available through tidy(), and summary(): conf_level, transform_post, etc.
• Bayesian posterior distribution summaries (median, mean, HDI, quantiles) can be customized using global options. See ?comparisons

Renamed functions (backward-compatibility is maintained by keeping the old function names as aliases):

• marginaleffects() -> slopes()
• posteriordraws() -> posterior_draws()
• marginalmeans() -> marginal_means()
• plot_cap() -> plot_predictions()
• plot_cme() -> plot_slopes()
• plot_cco() -> plot_comparisons()

Bug fixes:

• Incorrect results: In 0.8.1, plot_*() the threenum and minmax labels did not correspond to the correct numeric values.
• Fix corner case for slopes when the dataset includes infinite values.
• mlogit error with factors.
• The vcov argument now accepts functions for most models.

Other:

• Removed major performance bottleneck for slopes()

0.8.1

• deltamethod() can run hypothesis tests on objects produced by the comparisons(), marginaleffects(), predictions(), and marginalmeans() functions. This feature relies on match.call(), which means it may not always work when used programmatically, inside functions and nested environments. It is generally safer and more efficient to use the hypothesis argument.
• plot_cme() and plot_cco() accept lists with user-specified values for the regressors, and can display nice labels for shortcut string-functions like “threenum” or “quartile”.
• posterior_draws: new shape argument to return MCMC draws in various formats, including the new rvar structure from the posterior package.
• transform_avg function gets printed in summary() output.
• transform_post and transform_avg support string shortcuts: “exp” and “ln”
• Added support for mlm models from lm(). Thanks to Noah Greifer.

Bug fixes:

• hypothesis argument with bayesian models and tidy() used to raise an error.
• Missing values for some regressors in the comparisons() output for brms models.

0.8.0

Breaking change:

• The interaction argument is deprecated and replaced by the cross argument. This is to reduce ambiguity with respect to the interaction argument in emmeans, which does something completely different, akin to the difference-in-differences illustrated in the Interactions vignette.

71 classes of models supported, including the new:

• rms::ols
• rms::lrm
• rms::orm

New features:

• Plots: plot_cme(), plot_cap(), and plot_cco() are now much more flexible in specifying the comparisons to display. The condition argument accepts lists, functions, and shortcuts for common reference values, such as “minmax”, “threenum”, etc.
• variables argument of the comparisons() function is more flexible:
  • Accepts functions to specify custom differences in numeric variables (e.g., forward and backward differencing).
  • Can specify pairs of factors to compare in the variables argument of the comparisons function.
• variables argument of the predictions() function is more flexible:
  • Accepts shortcut strings, functions, and vectors of arbitrary length.
• Integrate out random effects in bayesian brms models (see Bayesian analysis vignette)

New vignettes:

• Experiments
• Extending marginal effects
• Integrating out random effects in bayesian models

Bug fixes and minor improvements:

• The default value of conf_level in summary() and tidy() is now NULL, which inherits the conf_level value in the original comparisons/marginaleffects/predictions calls.
• Fix typo in function names for missing “lnratioavgwts”
• Interactions with fixest::i() are parsed properly as categorical variables
• For betareg objects, inference can now be done on all coefficients using deltamethod(). previously only the location coefficients were available.
• For objects from crch package, a number of bugs have been fixed; standard errors should now be correct for deltamethod(), marginaleffects(), etc.
• Fixed a bug in the tidy() function for glmmTMB models without random effects, which caused all t statistics to be identical.

0.7.1

• New supported model class: gamlss. Thanks to Marcio Augusto Diniz.
• marginalmeans() accepts a wts argument with values: “equal”, “proportional”, “cells”.
• by argument
  • accepts data frames for complex groupings.
  • in marginalmeans only accepts data frames.
  • accepts “group” to group by response level.
  • works with bayesian models.
• byfun argument for the predictions() function to aggregate using different functions.
• hypothesis argument
  • The matrix column names are used as labels for hypothesis tests.
  • Better labels with “sequential”, “reference”, “pairwise”.
  • new shortcuts “revpairwise”, “revsequential”, “revreference”
• wts argument is respected in by argument and with *avg shortcuts in the transform_pre argument.
• tidy.predictions() and tidy.marginalmeans() get a new transform_avg argument.
• New vignettes:
  • Unit-level contrasts in logistic regressions. Thanks to @arthur-albuquerque.
  • Python Numpy models in marginaleffects. Thanks to timpipeseek.
  • Bootstrap example in standard errors vignette.

0.7.0

Breaking changes:

• by is deprecated in summary() and tidy(). Use the same by argument in the main functions instead: comparisons(), marginaleffects(), predictions()
• Character vectors are no longer supported in the variables argument of the predictions() function. Use newdata="fivenum" or “grid”, “mean”, or “median” instead.

Critical bug fix:

• Contrasts with interactions were incorrect in version 0.6.0. The error should have been obvious to most analysts in most cases (weird-looking alignment). Thanks to @vmikk.

New supported packages and models:

• survival::clogit
• biglm: The main quantities can be computed, but not the delta method standard errors. See https://github.com/vincentarelbundock/marginaleffects/issues/387

New vignette:

• Elasticity
• Frequently Asked Questions

New features:

• Elasticity and semi-elasticity using the new slope argument in marginaleffects(): eyex, dyex, eydx
• datagrid() accepts functions: datagrid(newdata = mtcars, hp = range, mpg = fivenum, wt = sd)
• New datagridcf() function to create counterfactual datasets. This is a shortcut to the datagrid() function with default to grid_type = "counterfactual"
• New by arguments in predictions(), comparisons(), marginaleffects()
• New newdata shortcuts: “tukey”, “grid”
• New string shortcuts for transform_pre in comparisons()
• marginalmeans() now back transforms confidence intervals when possible.
• vcov argument string shortcuts are now case-insensitive
• The default contrast in comparisons() for binary predictors is now a difference between 1 and 0, rather than +1 relative to baseline.
• documentation improvements

0.6.0

New supported packages and models:

• tidymodels objects of class tidy_model are supported if the fit engine is supported by marginaleffects.

New function:

• deltamethod(): Hypothesis tests on functions of parameters
• plot_cco(): Plot conditional contrasts

New arguments:

• hypothesis for hypothesis tests and custom contrasts
• transform_post in predictions()
• wts argument in predictions() only affects average predictions in tidy() or summary().

New or improved vignettes:

• Hypothesis Tests and Custom Contrasts using the Delta Method: https://marginaleffects.com/vignettes/hypothesis.html
• Multiple Imputation: https://marginaleffects.com/vignettes/multiple_imputation.html
• Causal Inference with the g-Formula: https://marginaleffects.com/vignettes/gcomputation.html (Thanks to Rohan Kapre for the idea)

Deprecated or renamed arguments:

• contrast_factor and contrast_numeric arguments are deprecated in comparisons(). Use a named list in the variables argument instead. Backward compatibility is maintained.
• The transform_post argument in tidy() and summary() is renamed to transform_avg to disambiguate against the argument of the same name in comparisons(). Backward compatibility is preserved.

Misc:

• tidy.predictions() computes standard errors using the delta method for average predictions
• Support gam models with matrix columns.
• eps in marginaleffects() is now “adaptive” by default: it equals 0.0001 multiplied the range of the predictor variable
• comparisons() now supports “log of marginal odds ratio” in the transform_pre argument. Thanks to Noah Greifer.
• New transform_pre shortcuts: dydx, expdydx
• tidy.predictions() computes standard errors and confidence intervals for linear models or GLM on the link scale.

0.5.0

Breaking changes:

• type no longer accepts a character vector. Must be a single string.
• conf.int argument deprecated. Use vcov = FALSE instead.

New supported packages and models:

• mlogit
• mhurdle
• tobit1
• glmmTMB

New features:

• interaction argument in comparisons() to compute interactions between contrasts (cross-contrasts).
• by argument in tidy() and summary() computes group-average marginal effects and comparisons.
• transform_pre argument can define custom contrasts between adjusted predictions (e.g., log adjusted risk ratios). Available in comparisons().
• transform_post argument allows back transformation before returning the final results. Available in comparisons(), marginalmeans(), summary(), tidy().
• The variables argument of the comparisons() function accepts a named list to specify variable-specific contrast types.
• Robust standard errors with the vcov argument. This requires version 0.17.1 of the insight package.
  • sandwich package shortcuts: vcov = "HC3", "HC2", "NeweyWest", and more.
  • Mixed effects models: vcov = "satterthwaite" or "kenward-roger"
  • One-sided formula to clusters: vcov = ~cluster_variable
  • Variance-covariance matrix
  • Function which returns a named squared matrix
• marginalmeans() allows interactions
• Bayesian Model Averaging for brms models using type = "average". See vignette on the marginaleffects website.
• eps argument for step size of numerical derivative
• marginaleffects and comparisons now report confidence intervals by default.
• New dependency on the data.table package yields substantial performance improvements.
• More informative error messages and warnings
• Bug fixes and performance improvements

New pages on the marginaleffects website: https://marginaleffects.com/

• Alternative software packages
• Robust standard errors (and more)
• Performance tips
• Tables and plots
• Multinomial Logit and Discrete Choice Models
• Generalized Additive Models
• Mixed effects models (Bayesian and Frequentist)
• Transformations and Custom Contrasts: Adjusted Risk Ratio Example

Argument name changes (backward compatibility is preserved:

• Everywhere:
  • conf.level -> conf_level
• datagrid():
  • FUN.factor -> FUN_factor (same for related arguments)
  • grid.type -> grid_type

0.4.1

New supported packages and models:

• stats::loess
• sampleSelection::selection
• sampleSelection::heckit

Misc:

• mgcv::bam models allow exclude argument.
• Gam models allow include_smooth argument.
• New tests
• Bug fixes

0.4.0

New function:

• comparisons() computes contrasts

Misc:

• Speed optimizations
• predictions() and plot_cap() include confidence intervals for linear models
• More robust handling of in-formula functions: factor(), strata(), mo()
• Do not overwrite user’s ggplot2::theme_set() call

0.3.4

• Bug fixes

0.3.3

New supported models:

• mclogit::mclogit
• robust::lmRob
• robustlmm::rlmer
• fixest confidence intervals in predictions

Misc:

• Support modelbased::visualisation_matrix in newdata without having to specify x explicitly.
• tidy.predictions() and summary.predictions() methods.
• Documentation improvements.
• CRAN test fixes

0.3.2

Support for new models and packages:

• brglm2::bracl
• mclogit::mblogit
• scam::scam
• lmerTest::lmer

Misc:

• Drop numDeriv dependency, but make it available via a global option: options(“marginaleffects_numDeriv” = list(method = “Richardson”, method.args = list(eps = 1e-5, d = 0.0001)))
• Bugfixes
• Documentation improvements
• CRAN tests

0.3.1

documentation bugfix

0.3.0

Breaking changes:

• predictions returns predictions for every observation in the original dataset instead of newdata=datagrid().
• marginalmeans objects have new column names, as do the corresponding tidy and summary outputs.

New supported packages and models:

• brms::brm
• rstanarm::stanglm
• brglm2::brmultinom
• MASS::glmmPQL
• aod::betabin

Misc:

• datagrid function supersedes typical and counterfactual with the grid.type argument. The typical and counterfactual functions will remain available and exported, but their use is not encouraged.
• posterior_draws function can be applied to a predictions or a marginaleffects object to extract draws from the posterior distribution.
• marginalmeans standard errors are now computed using the delta method.
• predictions standard errors are now computed using the delta method when they are not available from insight::get_predicted.
• New vignette on Bayesian models with brms
• New vignette on Mixed effects models with lme4
• If the data.table package is installed, marginaleffects will automatically use it to speed things up.
• Contrast definition reported in a separate column of marginaleffects output.
• Safer handling of the type argument.
• Comprehensive list of supported and tests models on the website.
• Many bug fixes
• Many new tests, including several against emmeans

0.2.0

Breaking change:

• data argument becomes newdata in all functions.

New supported packages and models:

• lme4:glmer.nb
• mgcv::gam
• ordinal::clm
• mgcv

marginalmeans:

• New variables_grid argument

predictions:

• Support mgcv

plot_cap

• New type argument

Misc:

• New validity checks and tests

0.1.0

First release. Bravo!

Thanks to Marco Avina Mendoza, Resul Umit, and all those who offered comments and suggestions.