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parsnip 1.1.0
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@topepo topepo released this 12 Apr 17:12
v1.1.0
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This release of parsnip contains a number of new features and bug fixes, accompanied by several optimizations that substantially decrease the time to fit() and predict() with the package.

Improvements to "glmnet" engine interfaces

  • glmnet models fitted with base-R family objects are now supported for linear_reg(), logistic_reg(), and multinomial_reg() (#890).

  • multi_predict() methods for linear_reg(), logistic_reg(), and multinom_reg() models fitted with the "glmnet" engine now check the type better and error accordingly (#900).

  • .organize_glmnet_pred() now expects predictions for a single penalty value (#876).

Survival analysis

  • The time argument to predict_survival() and predict_hazard() is deprecated in favor of the new eval_time argument (#936).

  • Added several internal functions (to help work with Surv objects) as a standalone file that can be used in other packages via usethis::use_standalone("tidymodels/parsnip"). These changes provide tooling for downstream packages to handle inverse probability censoring weights (#893, #897, #937).

  • An internal method for generating inverse probability of censoring weights (IPCW) of Graf et al (1999) is available via .censoring_weights_graf().

Bug fixes

  • Made fit() behave consistently with respect to missingness in the classification setting. Previously, fit() erroneously raised an error about the class of the outcome when there were no complete cases, and now always passes along complete cases to be handled by the modeling function (#888).

  • Fixed bug where model fits with engine = "earth" would fail when the package's namespace hadn't been attached (#251).

  • Fixed bug where model fits with factor predictors and engine = "kknn" would fail when the package's namespace hadn't been attached (#264).

  • Fixed bug with prediction from a boosted tree model fitted with "xgboost" using a custom objective function (#875).

Other changes

  • Implemented a number of optimizations in parsnip's backend that substantially decrease evaluation time to fit() and predict() (#901, #902, #910, #921, #929, #923, #931, #932, #933).

  • logistic_reg() will now warn at fit() when the outcome has more than two levels (#545).

  • Rather than being implemented in each method, the check for the new_data argument being mistakenly passed as newdata to multi_predict() now happens in the generic. Packages re-exporting the multi_predict() generic and implementing now-duplicate checks may see new failures and can remove their own analogous checks. This check already existed in all predict() methods (via predict.model_fit()) and all parsnip multi_predict() methods (#525).

  • Functions now indicate what class the outcome was if the outcome is the wrong class (#887).

  • The minimum version for R is now 3.5 (#926).

  • Moved forward with the deprecation of req_pkgs() in favor of required_pkgs(). The function will now error (#871).

  • Transitioned all soft-deprecations that were at least a year old to warn-deprecations. These changes apply to fit_control(), surv_reg(), varying(), varying_args(), and the "liquidSVM" engine.

  • Various bug fixes and improvements to documentation.

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