latentcor: Fast Computation of Latent Correlations for Mixed Data
Mingze Huang, Grace Yoon, Christian M uuml ller, Irina Gaynanova · 2021
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) . The latter method uses multi-linear interpolation originally implemented in the R package .