The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <doi:10.48550/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>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.
Version: 2.0.1 Depends: R (≥ 3.0.0) Imports: stats, pcaPP, fMultivar, mnormt, Matrix, MASS, heatmaply, ggplot2, plotly, graphics, geometry, doFuture, foreach, future, doRNG, microbenchmark Suggests: rmarkdown, markdown, knitr, testthat (≥ 3.0.0), lattice, cubature, plot3D, covr Published: 2022-09-05 DOI: 10.32614/CRAN.package.latentcor Author: Mingze Huang [aut, cre], Grace Yoon [aut], Christian Müller [aut], Irina Gaynanova [aut] Maintainer: Mingze Huang <mingzehuang at gmail.com> License: GPL-3 NeedsCompilation: yes Materials: README NEWS CRAN checks: latentcor results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=latentcor to link to this page.
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