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Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen' and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.

copied from cf-post-staging / r-optmatch
Type Size Name Uploaded Downloads Labels
conda 998.6 kB | win-64/r-optmatch-0.10.6-r41ha856d6a_1.conda  1 year and 11 months ago 528 main
conda 1003.2 kB | osx-64/r-optmatch-0.10.6-r42h64b2c41_1.conda  1 year and 11 months ago 401 main
conda 1008.0 kB | osx-64/r-optmatch-0.10.6-r43h64b2c41_1.conda  1 year and 11 months ago 407 main
conda 1021.4 kB | linux-64/r-optmatch-0.10.6-r43ha503ecb_1.conda  1 year and 11 months ago 1891 main
conda 1015.0 kB | linux-64/r-optmatch-0.10.6-r42ha503ecb_1.conda  1 year and 11 months ago 1845 main
conda 997.1 kB | win-64/r-optmatch-0.10.6-r41ha856d6a_0.conda  1 year and 11 months ago 543 main
conda 1003.7 kB | osx-64/r-optmatch-0.10.6-r42h64b2c41_0.conda  1 year and 11 months ago 416 main
conda 1015.0 kB | linux-64/r-optmatch-0.10.6-r42ha503ecb_0.conda  1 year and 11 months ago 1844 main

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