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: 2.1.1
Imports: glmnet, gmp
Suggests: nnet, knitr, mice, rmarkdown, testthat, xgboost
Published: 2021-12-07
DOI: 10.32614/CRAN.package.FLAME
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
URL: https://almost-matching-exactly.github.io,https://vittorioorlandi.github.io/
NeedsCompilation: no
In views: CausalInference
CRAN checks: FLAME results


Reference manual: FLAME.pdf
Vignettes: intro_to_AME


Package source: FLAME_2.1.1.tar.gz
Windows binaries: r-devel: FLAME_2.1.1.zip, r-release: FLAME_2.1.1.zip, r-oldrel: FLAME_2.1.1.zip
macOS binaries: r-release (arm64): FLAME_2.1.1.tgz, r-oldrel (arm64): FLAME_2.1.1.tgz, r-release (x86_64): FLAME_2.1.1.tgz, r-oldrel (x86_64): FLAME_2.1.1.tgz
Old sources: FLAME archive


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