Utilizing model-based clustering (unsupervised)
for fMRI data especially in a distributed manner. The methods
includes 2D and 3D clustering analyses and segmentation analyses for
fMRI signals where p-values are significant levels of active voxels
which respond to stimulate of interesting. The analyses are
mainly identifying active voxels/signals from normal brain behaviors.
Workflows are also implemented utilizing high performance techniques.
Arguments
Details
Package:
MixfMRI
Type:
Package
License:
GPL (>= 2)
LazyLoad:
yes
The main function of this package is fclust() that implements
model-based clustering algorithm for fMRI signal data and provides
unsupervised clustering results for the data. Several workflows implemented
with high-performance computing techniques are also built in for automatically
process clustering, hypothesis, cluster merging, and visualizations.
# NOT RUN {library(MixfMRI, quietly = TRUE)
# }# NOT RUN { demo(fclust3d,'MixfMRI',ask=FALSE,echo=FALSE)
demo(fclust2d,'MixfMRI',ask=FALSE,echo=FALSE)
# }