CCAGFA: Bayesian Canonical Correlation Analysis and Group Factor Analysis

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Variational Bayesian algorithms for learning canonical correlation analysis (CCA), inter-battery factor analysis (IBFA), and group factor analysis (GFA). Inference with several random initializations can be run with the functions CCAexperiment() and GFAexperiment().

Author
Seppo Virtanen [aut, cre], Eemeli Leppaaho [aut], Arto Klami [aut]
Date of publication
2015-12-17 16:08:55
Maintainer
Seppo Virtanen <s.virtanen@warwick.ac.uk>
License
GPL (>= 2)
Version
1.0.8
URLs

View on CRAN

Man pages

CCAcorr
Compute correlation between the views
CCAGFA-package
CCAGFA: Bayesian canonical correlation analysis (BCCA),...
getDefaultOpts
Get default options for BIBFA
GFA
Estimate a Bayesian IBFA/CCA/GFA model
GFApred
Predict samples of one view given the other(s)
GFAsample
Generate data from CCA/BIBFA/GFA model
GFAtrim
Simplify a CCA/BIBFA/GFA model
gradE
Compute the cost function and its gradient
gradEuv
Compute the cost function and its gradient

Files in this package

CCAGFA
CCAGFA/inst
CCAGFA/inst/CITATION
CCAGFA/NAMESPACE
CCAGFA/demo
CCAGFA/demo/00Index
CCAGFA/demo/CCAGFAexample.R
CCAGFA/NEWS
CCAGFA/R
CCAGFA/R/CCAGFA.R
CCAGFA/R/CCAGFAtools.R
CCAGFA/MD5
CCAGFA/DESCRIPTION
CCAGFA/man
CCAGFA/man/GFA.Rd
CCAGFA/man/getDefaultOpts.Rd
CCAGFA/man/GFAsample.Rd
CCAGFA/man/gradE.Rd
CCAGFA/man/CCAGFA-package.Rd
CCAGFA/man/gradEuv.Rd
CCAGFA/man/GFAtrim.Rd
CCAGFA/man/CCAcorr.Rd
CCAGFA/man/GFApred.Rd