latentcor: Fast Computation of Latent Correlations for Mixed Data

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) <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>.

Version: 1.2.0
Depends: R (≥ 3.0.0)
Imports: stats, pcaPP, fMultivar, mnormt, chebpol, Matrix, MASS, heatmaply, ggplot2, plotly, graphics
Suggests: microbenchmark, rmarkdown, markdown, knitr, testthat (≥ 3.0.0), covr
Published: 2021-10-31
Author: Mingze Huang ORCID iD [aut, cre], Grace Yoon ORCID iD [aut], Christian M&uuml;ller ORCID iD [aut], Irina Gaynanova ORCID iD [aut]
Maintainer: Mingze Huang <mingzehuang at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: latentcor results


Reference manual: latentcor.pdf
Vignettes: latentcor
Mathematical Framework for latentcor


Package source: latentcor_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): latentcor_1.2.0.tgz, r-release (x86_64): latentcor_1.2.0.tgz, r-oldrel: latentcor_1.2.0.tgz
Old sources: latentcor archive


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