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
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

Documentation:

Reference manual:	FLAME.pdf
Vignettes:	intro_to_AME

Downloads:

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

Linking:

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