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) . For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) . For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) . For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) .


Reference manual

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1.2.0 by Mingze Huang, 3 months ago

Browse source code at

Authors: Mingze Huang [aut, cre] , Grace Yoon [aut] , Christian Müller [aut] , Irina Gaynanova [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports stats, pcaPP, fMultivar, mnormt, chebpol, Matrix, MASS, heatmaply, ggplot2, plotly, graphics

Suggests microbenchmark, rmarkdown, markdown, knitr, testthat, covr

See at CRAN