PGICA-package: Parallel Group ICA Algorithm
In neuroconductor/PGICA: Parallel Group ICA Algorithm
Description
Details
Author(s)
References
Description
This package implements a Group ICA Alorithm which can run in parallel on clusters and multi-core personal computers. Unlike existing ICA algorithms, this parallel algorithm can analyze very big data. It can be used in many applications including signal processing and neuroimage data analysis.
Details
Package: PGICA
Type: Package
Version: 1.0
Date: 2014-11-12
License: GPL-2
The two main functions in this package are StVal and mica. mica is the main ICA function and StVal calculates initial values for mica.
Author(s)
Ani Eloyan, Shaojie Chen, Lei Huang, Huitong Qiu
Maintainer: <schen89@jhu.edu> Shaojie Chen
References
Ani Eloyan, Ciprian M. Crainiceanu and Brian S. Caffo: Likelihood Based Population Independent Component Analysis
neuroconductor/PGICA documentation built on May 23, 2019, 4:05 p.m.
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Description Details Author(s) References
Description
This package implements a Group ICA Alorithm which can run in parallel on clusters and multi-core personal computers. Unlike existing ICA algorithms, this parallel algorithm can analyze very big data. It can be used in many applications including signal processing and neuroimage data analysis.
Details
| Package: | PGICA |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2014-11-12 |
| License: | GPL-2 |
The two main functions in this package are StVal and mica. mica is the main ICA function and StVal calculates initial values for mica.
Author(s)
Ani Eloyan, Shaojie Chen, Lei Huang, Huitong Qiu
Maintainer: <schen89@jhu.edu> Shaojie Chen
References
Ani Eloyan, Ciprian M. Crainiceanu and Brian S. Caffo: Likelihood Based Population Independent Component Analysis
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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