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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>.
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Package details |
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| Author | Grace Yoon [aut] (ORCID: <https://orcid.org/0000-0003-3263-1352>), Mingze Huang [ctb] (ORCID: <https://orcid.org/0000-0003-3919-1564>), Irina Gaynanova [aut, cre] (ORCID: <https://orcid.org/0000-0002-4116-0268>) |
| Maintainer | Irina Gaynanova <irinagn@umich.edu> |
| License | GPL-3 |
| Version | 1.6.3 |
| Package repository | View on CRAN |
| Installation |
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