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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 |
| 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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These binaries (installable software) and packages are in development.
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