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) <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>.
|Author||Mingze Huang [aut, cre] (<https://orcid.org/0000-0003-3919-1564>), Grace Yoon [aut] (<https://orcid.org/0000-0003-3263-1352>), Christian Müller [aut] (<https://orcid.org/0000-0002-3821-7083>), Irina Gaynanova [aut] (<https://orcid.org/0000-0002-4116-0268>)|
|Maintainer||Mingze Huang <email@example.com>|
|Package repository||View on CRAN|
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