Package: fastHICA
Type: Package
Title: Hierarchical Independent Component Analysis: a Multi-Scale
Sparse Non-Orthogonal Data-Driven Basis
Version: 1.0.2
Date: 2015-05-12
Author: Piercesare Secchi, Simone Vantini, and Paolo Zanini
Maintainer: Paolo Zanini <paolo.zanini@polimi.it>
Depends: fastICA, energy
Description: It implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
License: GPL (>= 2)
Packaged: 2015-05-12 09:29:10 UTC; paolo
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-05-12 11:39:40
Package: AnalyzeFMRI
Version: 1.1-16
Date: 2013-03-19
Title: Functions for analysis of fMRI datasets stored in the ANALYZE or
NIFTI format.
Author: P Lafaye de Micheaux <lafaye@dms.umontreal.ca>, J L Marchini
<marchini@stats.ox.ac.uk>
Maintainer: P Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Depends: tcltk, R.matlab, fastICA
Description: Functions for I/O, visualisation and analysis of
functional Magnetic Resonance Imaging (fMRI) datasets stored in
the ANALYZE or NIFTI format.
License: GPL (>= 2)
Packaged: 2013-03-19 12:41:20 UTC; lafaye
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-03-19 15:38:10
Package: icaOcularCorrection
Type: Package
Title: Independent Components Analysis (ICA) based artifact correction.
Version: 3.0.0
Date: 2013-07-12
Depends: fastICA, mgcv
Author: Antoine Tremblay, NeuroCognitive Imaging Lab, Dalhousie University
Maintainer: Antoine Tremblay <trea26@gmail.com>
Description: Removes eye-movement and other types of known (i.e., recorded) or unknown (i.e., not recorded) artifacts using the fastICA package. The correction method proposed in this package is largely based on the method described in on Flexer, Bauer, Pripfl, and Dorffner (2005). The process of correcting electro- and magneto-encephalographic data (EEG/MEG) begins by running function ``icac'', which first performs independent components analysis (ICA) to decompose the data frame into independent components (ICs) using function ``fastICA'' from the package of the same name. It then calculates for each trial the correlation between each IC and each one of the noise signals -- there can be one or more, e.g., vertical and horizontal electro-oculograms (VEOG and HEOG), electro-myograms (EMG), electro-cardiograms (ECG), galvanic skin responses (GSR), and other noise signals. Subsequently, portions of an IC corresponding to trials at which the correlation between it and a noise signal was at or above threshold (set to 0.4 by default; Flexer et al., 2005, p. 1001) are zeroed-out in the source matrix, ``S''. The user can then identify which ICs correlate with the noise signals the most by looking at the summary of the ``icac'' object (using function ``summary.icac''), the scalp topography of the ICs (using function ``topo_ic''), the time courses of the ICs (using functions ``plot_tric'' and ``plot_nic''), and other diagnostic plots. Once these ICs have been identified, they can be completely zeroed-out using function ``update.icac'' and the resulting correction checked using functions ``plot_avgba'' and ``plot_trba''. Some worked-out examples with R code are provided in the package vignette.
Suggests: wavethresh
License: GPL-2
LazyLoad: yes
Packaged: 2013-07-12 15:48:11 UTC; antoine
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-07-12 18:51:58
Package: rgr
Type: Package
Title: Applied Geochemistry EDA
Version: 1.1.13
Date: 2016-01-05
Author: Robert G. Garrett
Maintainer: Robert G. Garrett <robert.garrett@canada.ca>
Depends: MASS, fastICA
Suggests: akima
Description: Geological Survey of Canada (GSC) functions for exploratory data analysis with applied geochemical data, with special application to the estimation of background ranges and identification of outliers, 'anomalies', to support mineral exploration and environmental studies. Additional functions are provided to support analytical data QA/QC, ANOVA for investigations of field sampling and analytical variability, and utility tasks. NOTE: function caplot() for concentration-area plots employs package 'akima', however, 'akima' is only licensed for not-for-profit use. Therefore, not-for-profit users of 'rgr' will have to independently make package 'akima' available through library(....); and use of function caplot() by for-profit users will fail.
License: GPL-2
NeedsCompilation: no
Packaged: 2016-01-07 13:38:54 UTC; THEBEASTTTTTTTTTTTT
Repository: CRAN
Date/Publication: 2016-01-07 15:40:29
providing 
.