censored 0.2.0
CRAN release: 2023-04-13
Cross-package changes with parsnip
The new
eval_time
argument replaces thetime
argument for the time points at which to predict survival probability and hazard. Thetime
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 forrand_forest()
for accelerated oblique random survival forests with the aorsf package (@bcjaeger, #211).Added the new
flexsurvspline
engine forsurvival_reg()
(@mattwarkentin, #213).
Bug fixes
Predictions of type
"linear_pred"
forsurvival_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"
forproportional_hazards(engine = "glmnet")
viamulti_predict()
now have the same sign as those viapredict()
(#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"
forsurvival_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()
forproportional_hazards()
models with the"survival"
and"glmnet"
engines,bag_tree()
models with the"rpart"
engine,decision_tree()
models with the"partykit"
engines, as well asrand_forest()
models with the"partykit"
engine (#221, #224).Added internal
survfit_summary_*()
helper functions (#216).
censored 0.1.1
CRAN release: 2022-09-30
For boosted trees with the
"mboost"
engine, survival probabilities can now be predicted fortime = -Inf
. This is always 1. Fortime = 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.