Description: solves Mahalanobis distance matrices

Title: Functions for Optimal Non-bipartite Matching
Author: Cole Beck [aut, cre], Bo Lu [aut], Robert Greevy [aut]
Authors@R: c(person("Cole", "Beck", role = c("aut", "cre"),
person("Robert", "Greevy", role = "aut"))
Description: This package provides functions for performing nonbipartite
matching and matched randomization. A "bipartite" matching utilizes two
separate groups, e.g. smokers being matched to nonsmokers or cases being
matched to controls. A "nonbipartite" matching creates mates from one big
group, e.g. 100 hospitals being randomized for a two-arm cluster randomized
trial or 5000 children who have been exposed to various levels of
secondhand smoke and are being paired to form a greater exposure vs. lesser
exposure comparison. At the core of a nonbipartite matching is a NxN
distance matrix for N potential mates. The distance between two units
expresses a measure of similarity or quality as mates (the lower the
better). The 'gendistance()' and 'distancematrix()' functions assist in
creating this. The 'nonbimatch()' function creates the matching that
minimizes the total sum of distances between mates; hence, it is referred
to as an "optimal" matching. The 'assign.grp()' function aids in performing
a matched randomization. Note the package can also perform bipartite
matching using the prevent option in 'gendistance()'.

Package: nbpMatching
Type: Package
Title: Functions for Optimal Non-Bipartite Matching
Version: 1.4.5
Date: 2015-05-13
Author: Cole Beck [aut, cre], Bo Lu [aut], Robert Greevy [aut]
Maintainer: Cole Beck <cole.beck@vanderbilt.edu>
Authors@R: c(person("Cole", "Beck", role = c("aut", "cre"),
email = "cole.beck@vanderbilt.edu"),
person("Bo", "Lu", role = "aut"),
person("Robert", "Greevy", role = "aut"))
Description: Perform non-bipartite matching and matched randomization. A
"bipartite" matching utilizes two separate groups, e.g. smokers being
matched to nonsmokers or cases being matched to controls. A "non-bipartite"
matching creates mates from one big group, e.g. 100 hospitals being
randomized for a two-arm cluster randomized trial or 5000 children who
have been exposed to various levels of secondhand smoke and are being
paired to form a greater exposure vs. lesser exposure comparison. At the
core of a non-bipartite matching is a N x N distance matrix for N potential
mates. The distance between two units expresses a measure of similarity or
quality as mates (the lower the better). The 'gendistance()' and
'distancematrix()' functions assist in creating this. The 'nonbimatch()'
function creates the matching that minimizes the total sum of distances
between mates; hence, it is referred to as an "optimal" matching. The
'assign.grp()' function aids in performing a matched randomization. Note
bipartite matching can be performed using the prevent option in
'gendistance()'.
License: GPL (>= 2)
LazyLoad: yes
Depends: methods, Hmisc
Imports: MASS
Suggests: testthat
URL: http://biostat.mc.vanderbilt.edu/wiki/Main/MatchedRandomization
NeedsCompilation: yes
Packaged: 2015-05-14 16:01:29 UTC; beckca
Repository: CRAN
Date/Publication: 2015-05-15 00:25:12