Skip to contents

aorsf 0.1.4 (unreleased)

aorsf 0.1.3

CRAN release: 2024-01-22

  • minor changes to partial dependence vignette to resolve code sanitization errors.

aorsf 0.1.2

CRAN release: 2024-01-15

aorsf 0.1.1

CRAN release: 2023-10-26

  • Fixed an uninitialized value for pd_type

  • Fixed various issues related to memory leaks

aorsf 0.1.0

CRAN release: 2023-10-13

  • Re-worked internal C++ routines following the design of ranger.

  • Re-worked how progress is printed to console when verbose_progress is TRUE, following the design of ranger. Messages now indicate the action being taken, the % complete, and the approximate time until finishing the action.

  • Improved variable importance, following the design of ranger. Importance is now computed tree-by-tree instead of by aggregate. Additionally, mortality is the type of prediction used for importance with survival trees, since mortality does not depend on pred_horizon.

  • Allowed multi-threading to be performed in orsf(), predict.orsf_fit(), and functions in the orsf_vi() and orsf_pd() family.

  • Allowed sampling without replacement and sampling a specific fraction of observations in orsf()

  • Included Harrell’s C-statistic as an option for assessing goodness of splits while growing trees.

  • Fixed an issue where an uninformative error message would occur when pred_horizon was > max(time) for orsf_summarize_uni. Thanks to @JyHao1 and @DustinMLong for finding this!

aorsf 0.0.7

CRAN release: 2023-01-12

  • Additional changes in internal testing to avoid problems with ATLAS

aorsf 0.0.6

CRAN release: 2023-01-06

  • Minor fix for internal tests that were failing when run on ATLAS

aorsf 0.0.5

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 ?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.

aorsf 0.0.4

CRAN release: 2022-11-07

  • 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.

aorsf 0.0.3

CRAN release: 2022-10-09

  • 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.

aorsf 0.0.2

CRAN release: 2022-09-05

  • Modified unit tests for compatibility with extra checks run through CRAN.

aorsf 0.0.1

CRAN release: 2022-08-23

  • 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.

aorsf 0.0.0.9000

  • Core features implemented: fit, interpret, and predict using oblique random survival forests.

  • Vignettes + Readme covering usage of core features.

  • Website hosted through GitHub pages, managed with pkgdown.