orsf() no longer throws errors or warnings when you
try to give it a single predictor. A note was added to the documentation
in the details of ?orsf that explains why using a single
predictor with orsf() is somewhat useless. This was done to
resolve
https://github.com/mlr-org/mlr3extralearners/issues/259.
predict.orsf_fit now accepts
pred_horizon = 0 and returns sensible values. Thanks to
@mattwarkentin
for the feature request.
added a function to perform variable selection,
orsf_vs().
Made variable importance consistent with respect to
group_factors. Originally, the output from
orsf would have ungrouped VI values while
orsf_vi would have grouped values. With this update,
orsf defaults to grouped values. The ungrouped values can
still be recovered.
Fixed an issue in orsf_pd functions where output
data were not being returned on the original scale.
orsf formulas now accepts Surv objects
(see https://github.com/ropensci/aorsf/issues/11)
Added verbose_progress input to orsf,
which prints messages to console indicating progress.
Allowance of missing values for orsf. Mean and mode
imputation is performed for observations with missing data. These values
can also be used to impute new data with missing values.
Centering and scaling of predictors is now done prior to growing the forest.
Included rOpenSci reviewers Christopher Jackson, Marvin N Wright,
and Lukas Burk in DESCRIPTION as reviewers. Thank
you!
Added clarification to docs about pros/cons of different variable importance techniques
Added regression tests for aorsf versus
obliqueRSF (they should be similar)
Additional support and tests for functions with long right hand sides
Updated out-of-bag vignette with more appropriate custom functions.
Allow status values in input data to be more general, i.e., not just 0 and 1.
Allow missing values in predict functions, including
partial dependence.
Added orsf_control_custom(), which allows users to
submit custom functions for identifying linear combinations of inputs
while growing oblique decision trees.
Added weights input to orsf, allowing
users to over or under fit orsf to specific data in their
training set.
Added chf and mort options to
predict.orsf_fit(). Mortality predictions are not fully
implemented yet - they are not supported in partial dependence or
out-of-bag error estimates. These features will be added in a future
update.
Core features implemented: fit, interpret, and predict using oblique random survival forests.
Vignettes + Readme covering usage of core features.
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