Title: Data Simulation Based on Latent Factors
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")))
Description: Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Imports: methods, mvtnorm, psych, rstudioapi
Luis Eduardo Garrido [aut] (<https://orcid.org/0000-0001-8932-6063>)

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"))
Imports: BBmisc, EGAnet, fspe, googledrive, ineq, methods, mlr,
mvtnorm, psych, qgraph, rstudioapi, xgboost
Luis Eduardo Garrido [aut] (<https://orcid.org/0000-0001-8932-6063>),

Imports: BBmisc, EGAnet, fspe, googledrive, ineq, Matrix, methods, mlr,

Title: Fast Computation of Latent Correlations for Mixed Data
Description: 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>.
Imports: stats, rmarkdown, pcaPP, fMultivar, mnormt, chebpol, Matrix,
utils, MASS, heatmaply, ggplot2, plotly, graphics, foreach,
parallel, doFuture, future, microbenchmark, doRNG
Suggests: markdown, knitr, testthat (>= 3.0.0), covr
Author: Mingze Huang [aut, cre] (<https://orcid.org/0000-0003-3919-1564>),