latentcor

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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