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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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.