GenericML 0.2.2
- 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
- Replaced
isa() with inherits() to avoid
reliance on R >= 4.1.
- Changed default in
parallel argument in
GenericML to FALSE.
GenericML 0.2.0
- 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
- 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
- Initial release on CRAN (Nov. 24, 2021)