Classification and regression

The orsf function can now be applied to continuous, binary, or categorical outcomes. This release also includes more support for partial dependence, including efficient multi-threading and some API changes that make it less tedious to use the orsf_pd functions.

Object oriented

Re-worked C++ following the design of the ranger package, making the codebase much more straightforward to maintain. Also, multi-threading has been added as a feature in addition to a few others (see https://docs.ropensci.org/aorsf/news/index.html for full description).

Missing data, scaling, verbosity, and more options in formula

• orsf formulas now accepts Surv objects (see #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: An R package for supervised learning using the oblique random survival forest

See https://docs.ropensci.org/aorsf/news/index.html, version 0.0.3

Accelerated and interpretable

Matches 0.0.1 on CRAN