Releases: CIRL-UNC/SuperLearnerMacro
Releases · CIRL-UNC/SuperLearnerMacro
v1.1.1
v1.1
Includes new/improved learners
- highly adaptive lasso (hal, ahal, ahalb)
- lasso with cross validation (cvlasso)
Features
- Cross validation now respects clustering/individuals with multiple records via "id" macro variable - this allows for survival analysis via discrete hazard functions! The SuperLearner macro now implements every primary feature of the R super learner package.
Bug fixes
- major: hard coded outcome variable names in some learners
- minor: improved macro variable scoping, which may have caused some errors in limited circumstances
Aesthetics
- improved responsiveness of output
v1.0
Official stable release in preparation for manuscript
Beta 2
Multiple bug fixes including full implementation of seed values, fixed ridge regression, several R learners
Features: added Ridge regression to estimate super learner fit, harmonized adaptive random forest super learner with other methods to give identical output
Beta release
Pre-release of version 1.0