latentcor: Fast Computation of Latent Correlations for Mixed Data (original) (raw)

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
License:	GPL-3
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	latentcor results [issues need fixing before 2025-12-01]

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