c(
person(given="Raluca", family="Gui",         email = "raluca.gui@gmail.com", role = c("cre","aut")),
person(given="Markus", family="Meierer",     email = "markus.meierer@business.uzh.ch", role = "aut"),
person(given="Rene",   family="Algesheimer", email = "market-research@business.uzh.ch", role = "aut"),
person(given="Patrik", family="Schilter",    email = "patrik.schilter@gmail.com",role = "aut"))

Several function to perform a Regional Frequency Analysis

Random Forest with Canonical Correlation Analysis
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"))
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. (2020) <arXiv:2011.11555>. 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.
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)

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.