Changes in version 0.1.6 (2025-12-11) - Corrected documentation indicating principal component analysis was a built-in option and fixed an issue where method="fast" was not giving the expected error inside of orsf_control functions (see https://github.com/ropensci/aorsf/pull/79). Thank you @emilyriederer! - added n_predictor_drop to orsf_vs(). Dropping one predictor at a time makes orsf_vs() slow for data with hundreds of predictors. Using a larger value for n_predictor_drop helps speed this up. The default value of n_predictor_drop is 1 to maintain backward compatibility. - orsf no longer throws hard errors if leaf_min_obs or leaf_min_events exceed the highest admissible value. Instead, a warning is returned and the input value is replaced by the highest admissible value. Changes in version 0.1.5 (2024-05-30) - fixed an issue where omitting NA values would cause an error in regression forests. Changes in version 0.1.4 (2024-05-03) - orsf_vs now returns a column that contains non-reference coded variable names (see https://github.com/ropensci/aorsf/pull/52). - orsf_vs no longer throws an error when n_predictor_min = 1 is used (see https://github.com/ropensci/aorsf/pull/58). - orsf_summarize_uni now allows specification of a class to summarize for oblique classification forests (see https://github.com/ropensci/aorsf/pull/57). - fixed an issue where orsf would throw an uninformative error when all predictors were categorical (see https://github.com/ropensci/aorsf/pull/56) - oblique random forests can now compute out-of-bag predictions on modified versions of their training data (see https://github.com/ropensci/aorsf/pull/54) - Setting oobag_pred_type to 'none' when growing a forest no longer necessitates the specification of pred_type when calling predict later (see https://github.com/ropensci/aorsf/pull/48). - Setting sample_fraction to 1 will no longer result in empty oobag_rows in the forest object (this would cause R to crash when the forest was passed to C++; see https://github.com/ropensci/aorsf/pull/48) - Re-worked the creation and maintenance of oobag_denom in C++ routines (see https://github.com/ropensci/aorsf/pull/48). - Restricted mean survival time is now used for pred_type = 'time' instead of median survival time (See https://github.com/ropensci/aorsf/pull/46). Changes in version 0.1.3 (2024-01-22) - minor changes to partial dependence vignette to resolve code sanitization errors. Changes in version 0.1.2 (2024-01-15) - Allowed option "time" for pred_type in predict and partial dependence to predict survival time (see https://github.com/ropensci/aorsf/issues/37). - Added pred_spec_auto() for more convenient specification of variables for partial dependence. - Partial dependence now runs much faster with multiple threads. - Added orsf_vint() to compute variable interaction scores using partial dependence. - Added orsf_update(), which can copy and modify an obliqueForest or modify it in place. - Added orsf_control functions for classification, regression, and survival (https://github.com/ropensci/aorsf/pull/25). - optimization implemented for matrix multiplication during prediction (https://github.com/ropensci/aorsf/pull/20) Changes in version 0.1.1 (2023-10-26) - Fixed an uninitialized value for pd_type - Fixed various issues related to memory leaks Changes in version 0.1.0 (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! Changes in version 0.0.7 (2023-01-12) - Additional changes in internal testing to avoid problems with ATLAS Changes in version 0.0.6 (2023-01-06) - Minor fix for internal tests that were failing when run on ATLAS Changes in version 0.0.5 (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. Changes in version 0.0.4 (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. Changes in version 0.0.3 (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. Changes in version 0.0.2 (2022-09-05) - Modified unit tests for compatibility with extra checks run through CRAN. Changes in version 0.0.1 (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. Changes in version 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.