scca

scca-package: Sparse canonical covariance analysis. scca is used to perform sparse canonical covariance analysis (SCCA). scca3 is the extension of scca to address 3 sets of variables on the same set of subjects.

Keywords for this software

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References in zbMATH (referenced in 7 articles , 1 standard article )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Lee, Woojoo; Do Ha, Il; Noh, Maengseok; Lee, Donghwan; Lee, Youngjo: A review on recent advances and applications of h-likelihood method (2021)
  2. Wang, Wenjia; Zhou, Yi-Hui: Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (2021)
  3. Kwon, Sunghoon; Oh, Seungyoung; Lee, Youngjo: The use of random-effect models for high-dimensional variable selection problems (2016)
  4. Ng, Chi Tim; Oh, Seungyoung; Lee, Youngjo: Going beyond oracle property: selection consistency and uniqueness of local solution of the generalized linear model (2016)
  5. Wang, Y. X. Rachel; Jiang, Keni; Feldman, Lewis J.; Bickel, Peter J.; Huang, Haiyan: Inferring gene-gene interactions and functional modules using sparse canonical correlation analysis (2015)
  6. Lee, Youngjo; Oh, Hee-Seok: A new sparse variable selection via random-effect model (2014)
  7. Lee, Woojoo; Lee, Donghwan; Lee, Youngjo; Pawitan, Yudi: Sparse canonical covariance analysis for high-throughput data (2011)