Uplift modeling aims at predicting the causal effect of an action such as a marketing campaign on a particular individual. In order to simplify the task for practitioners in uplift modeling, we propose a combination of tools that can be separated into the following ingredients: i) quantization, ii) visualization, iii) variable selection, iv) parameters estimation and, v) model validation. For more details, see <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.
Version: | 1.0.0 |
Depends: | R (≥ 3.1.2) |
Imports: | BiasedUrn, dplyr, glmnet, latticeExtra, lhs |
Suggests: | lattice |
Published: | 2021-01-06 |
Author: | Mouloud Belbahri, Olivier Gandouet, Alejandro Murua, Vahid Partovi Nia |
Maintainer: | Mouloud Belbahri <mouloud.belbahri at gmail.com> |
License: | GPL-2 | GPL-3 |
NeedsCompilation: | no |
In views: | CausalInference |
CRAN checks: | tools4uplift results |
Reference manual: | tools4uplift.pdf |
Package source: | tools4uplift_1.0.0.tar.gz |
Windows binaries: | r-devel: tools4uplift_1.0.0.zip, r-release: tools4uplift_1.0.0.zip, r-oldrel: tools4uplift_1.0.0.zip |
macOS binaries: | r-release (arm64): tools4uplift_1.0.0.tgz, r-oldrel (arm64): tools4uplift_1.0.0.tgz, r-release (x86_64): tools4uplift_1.0.0.tgz, r-oldrel (x86_64): tools4uplift_1.0.0.tgz |
Old sources: | tools4uplift archive |
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