Iacus, Stefano M., King, Gary, Porro, Giuseppe
Implementation of the Coarsened Exact Matching algorithm.

c(person("Stefano M.", "Iacus", email = "stefano.iacus@ec.europa.eu", role = c("aut", "cre")),
person("Gary", "King", email = "king@harvard.edu", role = c("aut")),
person("Giuseppe", "Porro", email = "giuseppe.porro@uninsubria.it", role = c("aut")),
person("Richard", "Nielsen", email = "rnielsen@mit.edu", role = c("aut"))
)
Implementation of the Coarsened Exact Matching algorithm discussed
along with its properties in
Iacus, King, Porro (2011) <DOI:10.1198/jasa.2011.tm09599>;
Iacus, King, Porro (2012) <DOI:10.1093/pan/mpr013> and
Iacus, King, Porro (2019) <DOI:10.1017/pan.2018.29>.
Stefano M. Iacus [aut, cre],
Gary King [aut],
Giuseppe Porro [aut],
Richard Nielsen [aut]

c(person("Stefano M.", "Iacus", email = "siacus@iq.harvard.edu", role = c("aut", "cre")),
person("Gary", "King", email = "king@harvard.edu", role = c("aut")),
person("Giuseppe", "Porro", email = "giuseppe.porro@uninsubria.it", role = c("aut")),
person("Richard", "Nielsen", email = "rnielsen@mit.edu", role = c("aut"))
)

nlme, lattice, randomForest, tcltk, combinat

c(person('Carlos', 'Relvas', role = c('aut'),
email = 'carlos.edu.relvas@gmail.com'),
person('Andre', 'Fujita', role = c('aut','cre'),
email = 'andrefujita@usp.br'))
Functions to fit the 'CemCO' algorithm, a model-based (Gaussian)
clustering algorithm that
removes/minimizes the effects of undesirable covariates during the
clustering process both in cluster centroid and in cluster covariance
structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).
clusteval, doParallel, nnet, rootSolve, foreach, MASS, mclust,
mvtnorm
Carlos Relvas [aut],
Andre Fujita [aut, cre]

Conditional expectation manifolds are an approach to
compute principal curves and surfaces.

Conditional expectation manifolds are an approach to compute principal curves and surfaces.

R (>= 2.10), plotrix, RColorBrewer, rgl, vegan

plotrix, rgl, vegan, stats, graphics, grDevices

Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.