{ "_id": "6a1efdeeb401979e73419cbe", "Package": "latentcor", "Type": "Package", "Title": "Fast Computation of Latent Correlations for Mixed Data", "Version": "2.0.2", "Authors@R": "c(\nperson(given = \"Mingze\",\nfamily = \"Huang\",\nrole = c(\"aut\"),\nemail = \"mingzehuang@gmail.com\",\ncomment = c(ORCID = \"0000-0003-3919-1564\")),\nperson(given = \"Grace\",\nfamily = \"Yoon\",\nrole = c(\"aut\"),\nemail = \"gyoon6067@gmail.com\",\ncomment = c(ORCID = \"0000-0003-3263-1352\")),\nperson(given = \"Christian\",\nfamily = \"Müller\",\nrole = c(\"aut\"),\nemail = \"christian.mueller@stat.uni-muenchen.de\",\ncomment = c(ORCID = \"0000-0002-3821-7083\")),\nperson(given = \"Irina\",\nfamily = \"Gaynanova\",\nrole = c(\"aut\", \"cre\"),\nemail = \"irinagn@umich.edu\",\ncomment = c(ORCID = \"0000-0002-4116-0268\")))", "Maintainer": "Irina Gaynanova ", "Description": "The first stand-alone R package for computation of latent\ncorrelation that takes into account all variable types\n(continuous/binary/ordinal/zero-inflated), comes with an\noptimized memory footprint, and is computationally efficient,\nessentially making latent correlation estimation almost as fast\nas rank-based correlation estimation. The estimation is based\non latent copula Gaussian models. For continuous/binary types,\nsee Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary\ntype, see Quan X., Booth J.G. and Wells M.T. (2018)\n. For truncated type or\nzero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I.\n(2020) . For approximation method\nof computation, see Yoon G., Müller C.L. and Gaynanova I.\n(2021) . 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