Marginaleffects

* 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.

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

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.

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`

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

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