Changes in version 0.3.4.9001                      

  - multi_predict() is now available for boost_tree() with the "mboost"
    engine over the trees submodel parameter (#290).

  - decision_tree() with the "rpart" engine now correctly returns the
    median survival time of the leaf's Kaplan-Meier curve for type =
    "time" predictions, instead of rpart's relative event rate (#331).

  - Prediction for proportional_hazards() with the "glmnet" engine no
    longer fails on data with factors when fitted through fit_xy()
    (#365).

  - The survival_prob_*() and hazard_*() helpers now validate their
    inputs and return more informative error messages when given an
    unusable object, new_data, or eval_time (#271).

                 Changes in version 0.3.4 (2026-04-04)                  

  - Adapted tests for hardhat 1.4.3 (#358).

                 Changes in version 0.3.3 (2025-02-14)                  

Breaking change

  - The format of quantile predictions now follows the new requirements
    in parsnip (#339, tidymodels/parsnip/#1209).

                 Changes in version 0.3.2 (2024-06-11)                  

  - censored now depends on survival >= 3.7-0 which allows us to use it
    also for predictions of survival probabilities at infinite
    evaluation time points. This means that: Survival probabilities at
    eval_time = Inf are now not always set to 0 and confidence intervals
    at infinite evaluation times are now not always set to NA. This
    applies to proportional_hazards()and bag_tree() models as well as
    models with the partykit engine, decision_tree() and rand_forest()
    (#320).

                 Changes in version 0.3.1 (2024-04-19)                  

  - Internal changes to the predict() methods for flexsurv models, in
    preparation for the upcoming flexsurv release (#317).

                 Changes in version 0.3.0 (2024-01-31)                  

New features

  - multi_predict() is now available for all prediction types for
    proportional_hazards() models with the "glmnet" engine, so newly
    also for type = "time" and type = "raw" (#277, #282).

  - Random forests with the "aorsf" engine can now predict survival
    time, i.e., predict(type = "time") is now available (#308).

Breaking change

  - The survival_prob_*(), survival_time_*(), and hazard_*() helper
    functions now all take a parsnip model_fit object as the main input,
    instead of an engine fit as was the case for some of them previously
    (#302).

Bug fixes

  - extract_fit_engine() now works properly for proportional hazards
    models fitted with the "glmnet" engine (#266).

  - multi_predict(type = "survival") for proportional_hazards(engine =
    "glmnet") models: when used with a single penalty value, this value
    is now included in the results. It was previously omitted (#267,
    #282).

  - proportional_hazards(engine = "glmnet") models now don't pretend to
    be able to deal with sparse matrices when they are not (#291).

  - Fixed a bug for proportional_hazards(engine = "glmnet") where
    prediction didn't work for a workflow() with a formula as the
    preprocessor (#264).

Other

  - The helper functions survival_time_coxnet() and
    survival_prob_coxnet() gain a multi argument to allow multiple
    values for penalty (#278, #279).

                 Changes in version 0.2.0 (2023-04-13)                  

Cross-package changes with parsnip

  - The new eval_time argument replaces the time argument for the time
    points at which to predict survival probability and hazard. The time
    argument has been deprecated (#244).

  - The matrix interface for fitting, fit_xy(), now works for censored
    regression models (#225, #234, #247, #251).

  - Improved error messages throughout the package (#248).

New engines

  - Added the new "aorsf" engine for rand_forest() for accelerated
    oblique random survival forests with the aorsf package (@bcjaeger,
    #211).

  - Added the new flexsurvspline engine for survival_reg()
    (@mattwarkentin, #213).

Bug fixes

  - Predictions of type "linear_pred" for survival_reg(engine =
    "flexsurv") are now on the correct scale for distributions where the
    natural scale and the unrestricted scale of the location parameter
    are identical, e.g. dist = "lnorm" (#229).

  - Predictions of type "linear_pred" for proportional_hazards(engine =
    "glmnet") via multi_predict() now have the same sign as those via
    predict() (#242).

  - Predictions of survival probability for survival_reg(engine =
    "flexsurv") for a single time point are now nested correctly (#254).

  - Predictions of survival probability for decision_tree(engine =
    "rpart") for a single observation now work (#256).

  - Predictions of type "quantile" for survival_reg(engine = "survival")
    for a single observation now work (#257).

  - Fixed a bug for printing coxnet models, i.e., proportional_hazards()
    models fitted with the "glmnet" engine (#249).

Internal changes

  - Predictions of survival probabilities are now calculated via
    summary.survfit() for proportional_hazards() models with the
    "survival" and "glmnet" engines, bag_tree() models with the "rpart"
    engine, decision_tree() models with the "partykit" engines, as well
    as rand_forest() models with the "partykit" engine (#221, #224).

  - Added internal survfit_summary_*() helper functions (#216).

                 Changes in version 0.1.1 (2022-09-30)                  

  - For boosted trees with the "mboost" engine, survival probabilities
    can now be predicted for time = -Inf. This is always 1. For time =
    Inf this now predicts a survival probability of 0 (#215).

  - Updated tests on model arguments and update() methods (#208).

  - Internal re-organisation of code (#206, 209).

  - Added a NEWS.md file to track changes to the package.