latentcor: Fast Computation of Latent Correlations for Mixed Data

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) <doi:10.1111/rssb.12168>. 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>.

Package details

AuthorMingze Huang [aut, cre] (<>), Grace Yoon [aut] (<>), Christian M&uuml;ller [aut] (<>), Irina Gaynanova [aut] (<>)
MaintainerMingze Huang <>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the latentcor package in your browser

Any scripts or data that you put into this service are public.

latentcor documentation built on Oct. 4, 2021, 5:07 p.m.