Marginaleffects

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()`

* `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.

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.

* 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.