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latentcor

Fast Computation of Latent Correlations for Mixed Data

v2.0.2 · Nov 26, 2025 · GPL-3

Description

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>.

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CRAN Check Status

14 OK
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r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK

Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Reverse Dependencies (1)

imports

Dependency Network

Dependencies Reverse dependencies pcaPP fMultivar mnormt Matrix MASS heatmaply ggplot2 plotly geometry doFuture foreach future doRNG microbenchmark mixedCCA latentcor

Version History

new 2.0.2 Mar 10, 2026
updated 2.0.2 ← 2.0.1 diff Nov 25, 2025
updated 2.0.1 ← 2.0.0 diff Sep 4, 2022
updated 2.0.0 ← 1.2.1 diff Aug 8, 2022
updated 1.2.1 ← 1.2.0 diff Jun 6, 2022
updated 1.2.0 ← 1.1.0 diff Oct 30, 2021
updated 1.1.0 ← 1.0.0 diff Oct 3, 2021
new 1.0.0 Aug 25, 2021