sf,stats,spatstat.geom, spatstat.model,spatstat.explore,
spatstat.linnet, spatstat (>=
2.0-0),graphics,grDevices,utils,snow,doSNOW,foreach,iterators,knitr

person("Jakub", "Nowosad", email = "nowosad.jakub@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-1057-3721"))
Provides color schemes for maps and other graphics
designed by 'CARTO' as described at <https://carto.com/carto-colors/>.
It includes four types of palettes: aggregation, diverging, qualitative,
and quantitative.
covr, MASS, testthat, vdiffr, sf, spData

Large, sparse optimal matching with refined covariate balance
Tools for large, sparse optimal matching of treated units
and control units in observational studies.  Provisions are
made for refined covariate balance constraints, which include
fine and near-fine balance as special cases.  Matches are
optimal in the sense that they are computed as solutions to
network optimization problems rather than greedy algorithms.

Large, Sparse Optimal Matching with Refined Covariate Balance

Tools for large, sparse optimal matching of treated units
and control units in observational studies.  Provisions are
made for refined covariate balance constraints, which include
fine and near-fine balance as special cases.  Matches are
optimal in the sense that they are computed as solutions to
network optimization problems rather than greedy algorithms.
See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879>
and Pimentel (2016), Obs. Studies 2(1):4-23.  The optmatch
package, which is useful for running many of the provided
functions, may be downloaded from Github at
<https://github.com/markmfredrickson/optmatch> if not available
on CRAN.

Tools for large, sparse optimal matching of treated units
and control units in observational studies.  Provisions are
made for refined covariate balance constraints, which include
fine and near-fine balance as special cases.  Matches are
optimal in the sense that they are computed as solutions to
network optimization problems rather than greedy algorithms.
See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879>
and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv
package, which provides an alternative solver for
the underlying network flow problems, carries an
academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.