NEWS
GenericML 0.2.2 (2022-06-18)
- Added class structure for accessor function objects
- Ensured consistency in documentation.
- Added new function,
heterogeneity_CLAN(), that investigates the presence of treatment effect heterogeneity along all CLAN variables.
- Added function
get_best() that returns the best learner.
- Changed behavior of
get_CLAN() to not plot ATE estimates when plot = TRUE.
GenericML 0.2.1 (2022-05-12)
- Replaced
isa() with inherits() to avoid reliance on R >= 4.1.
- Changed default in
parallel argument in GenericML to FALSE.
GenericML 0.2.0 (2022-05-06)
- Replaced
1:length(x)-like loops with safer seq()-based counterparts.
- Replaced
if() conditions comparing class() to string with the safer isa().
- Parallel computing is now also supported on Windows.
- Added a method
setup_plot() that returns the data frame that is used for plotting. Also, made the addition of ATEs in plots optional via the argument ATE in plot.GenericML().
- Added a function
GenericML_combine, which combines multiple GenericML objects into one.
- Implemented stratified sampling for sample splitting.
GenericML 0.1.1 (2021-12-07)
- Fixed a few typos in the documentation.
- Added conditions so that learners based on the package
glmnet in the tests and examples will be skipped on Solaris machines. Note that this does not prevent an error on Solaris because glmnet is still a Suggest of GenericML and glmnet v4.1.3 cannot be reliably installed on Solaris machines.
GenericML 0.1.0 (2021-11-24)
- Initial release on CRAN (Nov. 24, 2021)