NEWS

probably 1.2.0.9000

Add required_pkgs() methods to int_conformal_cv(), int_conformal_full(), int_conformal_quantile(), and int_conformal_split(). (#190)
Add butcher methods to int_conformal_cv(), int_conformal_full(), int_conformal_quantile(), and int_conformal_split(). (#194)

Updated unit tests for new ggplot2 release (#180).
Better error message when using one of the cal_validate_*() functions with a validation set (#182).

Significant refactoring of the code underlying the calibration functions. The user-facing APIs have not changed.

A new set of calibration functions are added that don’t calibrate the model (called cal_*_none()). These can be used as a reference implementation when tuning the calibration method (#174).
A new function bound_prediction() is available to constrain the values of a numeric prediction (#142).
cal_estimate_linear() and cal_estimate_logistic() now sets smooth = FALSE if data contains too few unique observations for splines to be fit, throwing a warning instead of an error (#167).
Transition from the magrittr pipe to the base R pipe.

A bug was fixed where cal_estimate_isotonic() always used a bootstrap sample.
Fixed a bug where non-standard names of class probability estimates resulted in an error for some calibration models (#145).
Bug fix for cal_plot_breaks() with binary classification with custom probability column names (#144).
Fixed an error in int_conformal_cv() when grouped resampling was used (#141).
Fixed an issue where the distance metric appeared inconsistently when using threshold_perf() with custom metric sets (@jrwinget, #149).

Fixed a bug where the grouping for calibration methods was sensitive to the type of the grouping variables (#127).

The conformal functions int_conformal_infer_*() were renamed to int_conformal_*().
predict.int_conformal_cv() now returns a .pred column that is the average prediction from the resampled models. The prediction intervals are centered on these.
Split conformal inference is available using int_conformal_split() and conformal quantile regression can be used with int_conformal_quantile().

Copyright holder changed to Posit Software PBC.
A set of calibration tools were added:
- The need for calibration can be visualized using the collection of cal_plot_*() functions.
- Calibration methods can be estimated with a family of cal_estimate_*() functions.
- To validate the calibrations using resampling, see the cal_validate_*() functions.
- cal_apply() can take a calibration model and apply it to a set of existing predictions.
Possible calibration tools:
- Binary classification methods: logistic regression, isotonic regression, and Beta calibration.
- Multiclass classification: multinomial, isotonic regression, and Beta calibration
- Regression: linear regression, isotonic regression
Based on the initial PR (#37) by Antonio R. Vargas, threshold_perf() now accepts a custom metric set (#25)
Two functions were added to compute prediction intervals for regression models via conformal inference:
- int_conformal_infer()
- int_conformal_infer_cv()

Max Kuhn is now the maintainer (#49).
Re-licensed package from GPL-2 to MIT. All copyright holders are RStudio employees and give consent.
Fixed a bug with how make_class_pred() and make_two_class_pred() validate the levels argument (#42).
threshold_perf() now has an explicit event_level argument rather than respecting the now deprecated yardstick.event_first global option (#45).
Bumped the minimum required R version to >=3.4.0 to align with the rest of the tidyverse.
Updated to testthat 3e (#44).

class_pred objects are now comparable and will be ordered by their levels. Equivocal values are generally considered to be the smallest value when ordering. NA values can be considered smaller if vec_order(na_value = "smallest") is used.

Suggest the modeldata package, which is where the lending_club dataset has been moved after being removed from recipes.
Use testthat::verify_output() on a test expecting a specific vctrs error to avoid failure on CRAN if that error changes in the future.

probably has been brought up to date with vctrs 0.2.0. This vctrs update had many function name changes, and required internal refactoring, but there should be minimal external changes.
The one user facing change comes with casting from one class_pred object to another class_pred, or to a factor. Where previously a warning would be thrown if x had levels that did not exist in to, an error is now generated. This is consistent with the vctrs behavior when converting from one factor to another.
```
x  <- class_pred(factor("a"))
to <- class_pred(factor("to"))
vec_cast(x, to)
#> Error: Lossy cast from <class_pred> to <class_pred>.
#> Locations: 1
```