censored 0.4.0

Breaking changes

bag_tree() with the "rpart" engine no longer accepts case weights. Fit the model without case weights instead (#390).
bag_tree() with the "rpart" engine now passes tree_depth, min_n, and cost_complexity on to the trees, so default fits now use the bag_tree() defaults of cost_complexity = 0 and min_n = 2. To restore the previous fits, set cost_complexity = 0.01 and min_n = 20 (#406).
boost_tree() with the "mboost" engine no longer accepts case weights because mboost doesn’t predict from a weighted fit. Fit the model without case weights instead (#363).
boost_tree() with the "mboost" engine no longer allows setting nu via set_engine(). Use learn_rate instead (#395).
boost_tree() with the "mboost" engine now warns and ignores control and tree_controls when set via set_engine(). Previously they silently overrode the main arguments, such as trees and tree_depth. Use the main arguments of boost_tree() instead (#414).
survival_prob_*() functions now error when given the defunct time argument. Use eval_time instead (#419).
survival_prob_coxph() no longer has the defunct x argument. Use object instead (#419).

boost_tree() with the "mboost" engine now passes learn_rate on to mboost::boost_control(nu), making it tunable. Previously it was silently ignored (#395).
multi_predict() is now available for boost_tree() with the "mboost" engine over the trees submodel parameter (#290).
null_model() gains a "survival" engine for censored regression, fitting a Kaplan-Meier curve via survival::survfit() (#353).
rand_forest() gains a "randomForestSRC" engine for censored regression, fitting a survival random forest via randomForestSRC::rfsrc() (#130).
rand_forest() gains a "ranger" engine for censored regression, fitting a survival random forest via ranger::ranger() (#131).

bag_tree() with the "rpart" engine now passes tree_depth, min_n, and cost_complexity, as well as other rpart::rpart.control() arguments set via set_engine(), on to the trees. Previously they were silently ignored (#406).
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).
proportional_hazards() with the "glmnet" engine now predicts on data with factors when fitted through fit_xy() (#365).
proportional_hazards() with the "survival" engine now predicts when a single strata() term contains more than one variable, e.g. strata(s1, s2) (#404).
survival_prob_*() and hazard_*() now validate their inputs and return more informative error messages when given an unusable object, new_data, or eval_time (#271).
survival_reg() with the "survival" engine and strata() terms now returns NA survival and hazard predictions for new data rows with a missing value in a stratification variable, instead of silently using another stratum’s scale (#383).
Engine-specific tuning parameters are now registered by censored instead of parsnip (#417).

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

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).

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

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).

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).

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).

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

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).

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).

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).

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).

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.