Changelog

Source: NEWS.md

marginaleffects 0.6.0.9000

New supported packages and models:

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

New vignette:

• Frequently Asked Questions

marginaleffects 0.6.02022-06-20

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 0.5.02022-05-17

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 0.4.12022-03-27

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 0.4.02022-03-13

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 0.3.42022-03-03

• Bug fixes

marginaleffects 0.3.32022-01-26

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 0.3.22022-01-18

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 0.3.12022-01-09

documentation bugfix

marginaleffects 0.3.02022-01-08

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 0.2.02021-10-18

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 0.1.02021-09-29

First release. Bravo!

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