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). 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 <>.

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 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: yes
Materials: README NEWS
CRAN checks: latentcor results


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


Package source: latentcor_2.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): latentcor_2.0.1.tgz, r-oldrel (arm64): latentcor_2.0.1.tgz, r-release (x86_64): latentcor_2.0.1.tgz, r-oldrel (x86_64): latentcor_2.0.1.tgz
Old sources: latentcor archive

Reverse dependencies:

Reverse imports: mixedCCA


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