c(
person("Alexander", "Christensen", email = "alexpaulchristensen@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9798-7037")),
person("Maria Dolores", "Nieto Canaveras", email = "mnietoca@nebrija.es", role = "aut"),
person("Hudson", "Golino", email = "hfg9s@virginia.edu", role = "aut", comment = c(ORCID = "0000-0002-1601-1447")),
person("Luis", "Eduardo Garrido", email = "luisgarrido@pucmm.edu", role = "aut", comment = c(ORCID = "0000-0001-8932-6063")),
person("Marcos", "Jimenez", role = "aut", email = "marcosjnezhquez@gmail.com"),
person("Francisco", "Abad", role = "ctb"),
person("Eduardo", "Garcia-Garzon", role = "ctb"),
person("Vithor", "Franco", role = "aut", email = "vithorfranco@gmail.com"))
BBmisc, EGAnet, fspe, googledrive, ineq, methods, mlr,
mvtnorm, psych, qgraph, rstudioapi, xgboost
Alexander Christensen [aut, cre]
(<https://orcid.org/0000-0002-9798-7037>),
Maria Dolores Nieto Canaveras [aut],
Hudson Golino [aut] (<https://orcid.org/0000-0002-1601-1447>),
Luis Eduardo Garrido [aut] (<https://orcid.org/0000-0001-8932-6063>),
Marcos Jimenez [aut],
Francisco Abad [ctb],
Eduardo Garcia-Garzon [ctb],
Vithor Franco [aut]

BBmisc, EGAnet, fspe, googledrive, ineq, Matrix, methods, mlr,
mvtnorm, psych, qgraph, rstudioapi, xgboost

Fast Computation of Latent Correlations for Mixed Data
c(
person(given = "Mingze",
family = "Huang",
role = c("aut", "cre"),
email = "mingzehuang@gmail.com",
comment = c(ORCID = "0000-0003-3919-1564")),
person(given = "Grace",
family = "Yoon",
role = c("aut"),
email = "gyoon6067@gmail.com",
comment = c(ORCID = "0000-0003-3263-1352")),
person(given = "Christian",
family = "M&uuml;ller",
role = c("aut"),
email = "christian.mueller@stat.uni-muenchen.de",
comment = c(ORCID = "0000-0002-3821-7083")),
person(given = "Irina",
family = "Gaynanova",
role = c("aut"),
email = "irinag@stat.tamu.edu",
comment = c(ORCID = "0000-0002-4116-0268")))
Estimation of latent correlations with mixed data types:continuous, binary, truncated (or zero-inflated) and ternary. The estimation is based on latent copula Gaussian models.
For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017) <doi:10.1111/rssb.12168>.
For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>.
For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>.
For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>.
stats, rmarkdown, pcaPP, fMultivar, mnormt, chebpol, Matrix,
utils, MASS, heatmaply, ggplot2, plotly, graphics, foreach,
parallel, doFuture, future, microbenchmark, doRNG
markdown, knitr, testthat (>= 3.0.0), covr