marginaleffects 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”.
• posteriordraws: 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.

marginaleffects 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.

marginaleffects 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.

marginaleffects 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

marginaleffects 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://vincentarelbundock.github.io/marginaleffects/articles/hypothesis.html
• Multiple Imputation: https://vincentarelbundock.github.io/marginaleffects/articles/multiple_imputation.html
• Causal Inference with the g-Formula: https://vincentarelbundock.github.io/marginaleffects/articles/gformula.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.

marginaleffects 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://vincentarelbundock.github.io/marginaleffects/

• 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

marginaleffects 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

marginaleffects 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

marginaleffects 0.3.4

• Bug fixes

marginaleffects 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

marginaleffects 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

marginaleffects 0.3.1

documentation bugfix

marginaleffects 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.
• posteriordraws 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

marginaleffects 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

marginaleffects 0.1.0

First release. Bravo!

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