mixedCCA: Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data

Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Getting started

Package details

AuthorGrace Yoon [aut] (<https://orcid.org/0000-0003-3263-1352>), Mingze Huang [ctb] (<https://orcid.org/0000-0003-3919-1564>), Irina Gaynanova [aut, cre] (<https://orcid.org/0000-0002-4116-0268>)
MaintainerIrina Gaynanova <irinag@stat.tamu.edu>
LicenseGPL-3
Version1.6.2
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("mixedCCA")

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mixedCCA documentation built on Sept. 10, 2022, 1:06 a.m.