In neuroconductor/PGICA: Parallel Group ICA Algorithm
Introduction
ICA (independent components analysis) algorithms are a collection of
statistical decompositions used for blind source separation. Our procedure
uses the procedures developed by Eloyan [@eloyan2013likelihood, @eloyan2013semiparametric]. ICA has become a standard tool in functional
neuroimaging using magnetic resonance imaging. This package was built with
that use in mind. However, it can be used generally.
Useage
neuroconductor/PGICA documentation built on May 23, 2019, 4:05 p.m.
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Introduction
ICA (independent components analysis) algorithms are a collection of statistical decompositions used for blind source separation. Our procedure uses the procedures developed by Eloyan [@eloyan2013likelihood, @eloyan2013semiparametric]. ICA has become a standard tool in functional neuroimaging using magnetic resonance imaging. This package was built with that use in mind. However, it can be used generally.
Useage
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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