FLAME: Interpretable Matching for Causal Inference

Efficient implementations of the algorithms in the Almost-Matching-Exactly framework for interpretable matching in causal inference. These algorithms match units via a learned, weighted Hamming distance that determines which covariates are more important to match on. For more information and examples, see the Almost-Matching-Exactly website.

Version: 2.0.0
Imports: dplyr, magrittr, mice, glmnet, gmp, rlang, tidyr, xgboost, devtools
Suggests: testthat, knitr, rmarkdown
Published: 2020-04-15
Author: Vittorio Orlandi [aut, cre], Sudeepa Roy [aut], Cynthia Rudin [aut], Alexander Volfovsky [aut]
Maintainer: Vittorio Orlandi <almost.matching.exactly at gmail.com>
BugReports: https://github.com/vittorioorlandi/FLAME/issues
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: FLAME results


Reference manual: FLAME.pdf
Vignettes: Introduction to FLAME
Package source: FLAME_2.0.0.tar.gz
Windows binaries: r-prerelease: FLAME_2.0.0.zip, r-release: FLAME_2.0.0.zip, r-oldrel: FLAME_2.0.0.zip
macOS binaries: r-prerelease: FLAME_2.0.0.tgz, r-release: FLAME_2.0.0.tgz, r-oldrel: FLAME_1.0.0.tgz
Old sources: FLAME archive


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