c (person ("Daniel", "Braithwaite", role = c ("aut", "cre"),
email = "contact.dtb@gmail.com"), person ("Kevin", "Keegan", role = "aut",
email = "keegan@mcs.anl.gov"), person (c ("University", "of"), "Chicago",
role = "cph"))
An analysis platform for metagenomics combining
specialized tools and workflows, easy handling of the BIOM
format, and transparent access to MG-RAST resources.  matR integrates
easily with other R packages and non-R software.
Daniel Braithwaite [aut, cre],
Kevin Keegan [aut],
University of Chicago [cph]

c(
person("Sean", "Kross", email = "sean@seankross.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5215-0316")),
person("Lucy", "D'Agostino McGowan", email = "lucydagostino@gmail.com", role = "aut", comment = c(ORCID = "0000-0001-7297-9359")),
person("Jeff", "Leek", email = "jtleek@gmail.com", role = "ldr", comment = c(ORCID = "0000-0002-2873-2671"))
)
Conveniently log everything you type into the R console. Logs are
are stored as tidy data frames which can then be analyzed using 'tidyverse'
style tools.
rstudioapi, tibble, readr, jsonlite, clipr, purrr, rlang

Multivariate matching in observational studies typically has two goals: 1. to construct
treated and control groups that have similar distribution of observed covariates and 2. to produce
matched pairs or sets that are homogeneous in a few priority variables. This packages implements a
network-based method built around a tripartite graph that can simultaneously achieve both goals. A
detailed 'RMarkdown' tutorial can be found at <https://github.com/bzhangupenn/match2C/tree/master/tutorial>.
mvnfast, stats, rcbalance, Rcpp (>= 1.0.3), utils

person("Bo", "Zhang", email = "bozhan@wharton.upenn.edu", role = c("aut", "cre"))
Multivariate matching in observational studies typically has two goals: 1. to construct
treated and control groups that have similar distribution of observed covariates and 2. to produce
matched pairs or sets that are homogeneous in a few priority variables. This packages implements a
network-flow-based method built around a tripartite graph that can simultaneously achieve both goals.
A detailed explanation of the workflow and numerous examples are given in the vignette.
ggplot2, mvnfast, rcbalance, Rcpp, stats, utils
dplyr, knitr, optmatch, RItools, rmarkdown