Package: RFCCA
Title: Random Forest with Canonical Correlation Analysis
Version: 1.0.10
Authors@R: 
    c(person(given = "Cansu", family = "Alakus", role = c("aut", "cre"), email = "cansu.alakus@hec.ca"),
      person(given = "Denis", family = "Larocque", role = c("aut"), email = "denis.larocque@hec.ca"),
      person(given = "Aurelie", family = "Labbe", role = c("aut"), email = "aurelie.labbe@hec.ca"),
      person(given = "Hemant", family = "Ishwaran", role = c("ctb"), comment = "Author of included randomForestSRC codes"),
      person(given = "Udaya B.", family = "Kogalur", role = c("ctb"), comment = "Author of included randomForestSRC codes"))
Description: Random Forest with Canonical Correlation Analysis (RFCCA) is a 
  random forest method for estimating the canonical correlations between two 
  sets of variables depending on the subject-related covariates. The trees are 
  built with a splitting rule specifically designed to partition the data to 
  maximize the canonical correlation heterogeneity between child nodes. The 
  method is described in Alakus et al. (2021) <doi:10.1093/bioinformatics/btab158>. RFCCA uses 
  'randomForestSRC' package (Ishwaran and Kogalur, 2020) by freezing at the 
  version 2.9.3. The custom splitting rule feature is utilised to apply the 
  proposed splitting rule.
Depends: R (>= 3.5.0)
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.0
Imports: CCA, PMA
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
URL: https://github.com/calakus/RFCCA
BugReports: https://github.com/calakus/RFCCA/issues
NeedsCompilation: yes
Packaged: 2023-03-05 23:13:52 UTC; cansualakus
Author: Cansu Alakus [aut, cre],
  Denis Larocque [aut],
  Aurelie Labbe [aut],
  Hemant Ishwaran [ctb] (Author of included randomForestSRC codes),
  Udaya B. Kogalur [ctb] (Author of included randomForestSRC codes)
Maintainer: Cansu Alakus <cansu.alakus@hec.ca>
Repository: CRAN
Date/Publication: 2023-03-05 23:30:02 UTC
