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censored 0.3.2

• 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).

censored 0.3.1

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

censored 0.3.0

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).

censored 0.2.0

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).

censored 0.1.1

• 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.

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