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).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).
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).
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.