CRAN release: 2023-01-12
- Additional changes in internal testing to avoid problems with ATLAS
CRAN release: 2023-01-06
- Minor fix for internal tests that were failing when run on ATLAS
CRAN release: 2022-12-14
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
?orsfthat explains why using a single predictor with
orsf()is somewhat useless. This was done to resolve https://github.com/mlr-org/mlr3extralearners/issues/259.
pred_horizon = 0and returns sensible values. Thanks to @mattwarkentin for the feature request.
added a function to perform variable selection,
Made variable importance consistent with respect to
group_factors. Originally, the output from
orsfwould have ungrouped VI values while
orsf_viwould have grouped values. With this update,
orsfdefaults to grouped values. The ungrouped values can still be recovered.
Fixed an issue in
orsf_pdfunctions where output data were not being returned on the original scale.
CRAN release: 2022-11-07
orsfformulas now accepts
Survobjects (see https://github.com/ropensci/aorsf/issues/11)
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.
CRAN release: 2022-10-09
Included rOpenSci reviewers Christopher Jackson, Marvin N Wright, and Lukas Burk in
DESCRIPTIONas reviewers. Thank you!
Added clarification to docs about pros/cons of different variable importance techniques
Added regression tests for
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
predictfunctions, including partial dependence.
CRAN release: 2022-09-05
- Modified unit tests for compatibility with extra checks run through CRAN.
CRAN release: 2022-08-23
orsf_control_custom(), which allows users to submit custom functions for identifying linear combinations of inputs while growing oblique decision trees.
orsf, allowing users to over or under fit
orsfto specific data in their training set.
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.