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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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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Reverse Dependencies (1)

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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 9, 2026