GenericML: Generic Machine Learning Inference

Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <doi:10.48550/arXiv.1712.04802>. This package's workhorse is the 'mlr3' framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <doi:10.48550/arXiv.1712.04802> for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported.

Version:	0.2.2
Depends:	ggplot2, mlr3, mlr3learners
Imports:	sandwich, lmtest, splitstackshape, stats, parallel, abind
Suggests:	glmnet, ranger, rpart, e1071, xgboost, kknn, DiceKriging, testthat (≥ 3.0.0)
Published:	2022-06-18
Author:	Max Welz [aut, cre], Andreas Alfons [aut], Mert Demirer [aut], Victor Chernozhukov [aut]
Maintainer:	Max Welz <welz at ese.eur.nl>
BugReports:	https://github.com/mwelz/GenericML/issues/
License:	GPL (≥ 3)
URL:	https://github.com/mwelz/GenericML/
NeedsCompilation:	no
Citation:	GenericML citation info
Materials:	NEWS
CRAN checks:	GenericML results

Documentation:

Reference manual:

GenericML.pdf

Downloads:

Package source:	GenericML_0.2.2.tar.gz
Windows binaries:	r-devel: GenericML_0.2.2.zip, r-release: GenericML_0.2.2.zip, r-oldrel: GenericML_0.2.2.zip
macOS binaries:	r-release (arm64): GenericML_0.2.2.tgz, r-oldrel (arm64): GenericML_0.2.2.tgz, r-release (x86_64): GenericML_0.2.2.tgz, r-oldrel (x86_64): GenericML_0.2.2.tgz
Old sources:	GenericML archive

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