gcv.smooth2d

<p>an interval for <code>optimize</code> function.</p>

interval

<p>Generate datasets with smoothing for MixfMRI simulations</p>

programming

Smoothing

Utilizing model-based clustering (unsupervised)
for functional magnetic resonance imaging (fMRI) data.
The developed methods (Chen and Maitra (2018, manuscript)) include
2D and 3D clustering analyses (for p-values with voxel locations) and
segmentation analyses (for p-values alone) for fMRI data where p-values
indicate significant level of activation responding to stimulate
of interesting. The analyses are mainly identifying active
voxel/signal associated with normal brain behaviors.
Analysis pipelines (R scripts) utilizing this package
(see examples in 'inst/workflow/') is also implemented with high
performance techniques.

Wei-Chen Chen

MixfMRI

Mixture fMRI Clustering Analysis

Ranjan Maitra

Dan Nettleton

Smoothing function

Generate datasets with smoothing for MixfMRI simulations — Smoothing

Generate datasets with smoothing for MixfMRI simulations

Smoothing: Generate datasets with smoothing for MixfMRI simulations

Description

Usage

Arguments

Value

Details

References