GPCSIV: GPCSIV, Generalized Principal Component of Symbolic Interval variables

This package implements an extension of principal component analysis (PCA) tailored to handle multiple data tables. It can handle Big Data in the sense that the variation in massive data can be described by intervals [a, b] and multiple tables.

AuthorBrahim Brahim and Sun Makosso-Kallyth <sun.makosso-kallyth@crchuq.ulaval.ca>
Date of publication2013-09-15 09:53:08
MaintainerBrahim Brahim <brahim.brahim@bigdatavisualizations.com>
LicenseGPL (>= 2)
Version0.1.0

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Files

GPCSIV
GPCSIV/NAMESPACE
GPCSIV/data
GPCSIV/data/video2.rda
GPCSIV/data/oils.rda
GPCSIV/data/video1.rda
GPCSIV/data/Judge3.rda
GPCSIV/data/Judge1.rda
GPCSIV/data/video3.rda
GPCSIV/data/Judge2.rda
GPCSIV/R
GPCSIV/R/gpca.R GPCSIV/R/Resdata.R GPCSIV/R/GPCSIV-internal.R
GPCSIV/MD5
GPCSIV/DESCRIPTION
GPCSIV/man
GPCSIV/man/oils.Rd GPCSIV/man/Judge2.Rd GPCSIV/man/Judge3.Rd GPCSIV/man/gpca.Rd GPCSIV/man/video1.Rd GPCSIV/man/GPCSIV.Rd GPCSIV/man/video2.Rd GPCSIV/man/video3.Rd GPCSIV/man/Judge1.Rd GPCSIV/man/Resdata.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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