Fast Computation of Latent Correlations for Mixed Data R package latentcor version 2.0.2 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>. Software 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 Irina Gaynanova <irinagn@umich.edu> Comprehensive R Archive Network (CRAN) Mingze Huang [aut] (ORCID: <https://orcid.org/0000-0003-3919-1564>), Grace Yoon [aut] (ORCID: <https://orcid.org/0000-0003-3263-1352>), Christian M&uuml;ller [aut] (ORCID: <https://orcid.org/0000-0002-3821-7083>), Irina Gaynanova [aut, cre] (ORCID: <https://orcid.org/0000-0002-4116-0268>) GPL-3 2025-11-26 application/tgz https://CRAN.R-project.org/package=latentcor doi:10.32614/CRAN.package.latentcor