c( person("Christian", "Sigg", email =
"christian@sigg-iten.ch", role = c("aut", "cre"), comment =
c(ORCID = "0000-0003-1067-9224")), person("R Core team", role =
"ctb", comment = "cancor interface and documentation"))
Two implementations of canonical correlation analysis
(CCA) that are based on iterated regression. By choosing the
appropriate regression algorithm for each data domain, it is
possible to enforce sparsity, non-negativity or other kinds of
constraints on the projection vectors. Multiple canonical
variables are computed sequentially using a generalized
deflation scheme, where the additional correlation not
explained by previous variables is maximized. 'nscancor' is
used to analyze paired data from two domains, and has the same
interface as the 'cancor' function from the 'stats' package
(plus some extra parameters). 'mcancor' is appropriate for
analyzing data from three or more domains. See
<http://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/>
and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more
details.
CCA, glmnet, MASS, PMA, roxygen2, testthat (>= 0.8)
Christian Sigg [aut, cre] (<https://orcid.org/0000-0003-1067-9224>),
R Core team [ctb] (cancor interface and documentation)

c( person("Christian", "Sigg", email =
"christian@sigg-iten.ch", role = c("aut", "cre")), person("R
Core team", role = "aut"))
This package implements two algorithms for canonical correlation
analysis (CCA) that are based on iterated regression
steps. By choosing the appropriate regression algorithm for each data
modality, it is possible to enforce sparsity, non-negativity or other kinds
of constraints on the projection vectors. Multiple canonical variables are
computed sequentially using a generalized deflation scheme, where the
additional correlation not explained by previous variables is maximized.
'nscancor' is used to analyze paired data from two domains, and has the same
interface as the 'cancor' function from the 'stats' package (plus some extra
parameters). 'mcancor' is appropriate for analyzing data from three or more
domains.
CCA, glmnet, MASS, PMA, testthat (>= 0.8), roxygen2
Christian Sigg [aut, cre],
R Core team [aut]