I apologize but the link to the Github page is: https://github.com/cwatson/brainGraph
Chris On Tue, Apr 11, 2017 at 7:37 PM, Chris Watson <cwa...@alum.mit.edu> wrote: > Dear Freesurfer users, (apologies for cross-post) > > I am pleased to announce the first major release of my R package > "brainGraph" (v1.0.0), a collection of functions for performing graph > theory analyses of brain MRI data. I chose R because it is free and is, in > my opinion, the best choice for just about any statistical analysis. It is > available on CRAN at https://cran.r-project.org/ > web/packages/brainGraph/index.html and development versions will be on my > Github page (link below). > > You can use it for gray matter covariance networks (cortical thickness, > volume, surface area, or LGI), DTI tractography data (FSL's "probtrackx2", > PANDA, TrackVis, etc.), and for resting-state fMRI (the Matlab toolbox > DPABI/DPARSF). > It is very heavily dependent on the fantastic R package "igraph" (see > igraph.org), which is based on C code and is quite a bit faster than many > other R applications. > > My Github page for the package is *MailScanner has detected a possible > fraud attempt from "email.tch.harvard.edu" claiming to be* > https://github.com/cwatson/brainGraph > <https://email.tch.harvard.edu/owa/redir.aspx?REF=AuYw-ae9TnjtRShTO7f-UJbUR68DDbXJHlMywLq1BKdgp0I_NYDUCAFodHRwczovL2dpdGh1Yi5jb20vY3dhdHNvbi9icmFpbkdyYXBo>. > At the bottom is the "README.md" file which provides some basic > information. Most important is the link to the User Guide, which has > extensive installation and usage information (warning: it is a direct PDF > link). It is very long but should be helpful. You will find code for > getting your data into R, and I have documented many analysis steps and > include multiple figures. I hope this is intuitive for both veteran and > novice R users. Additionally, there are links for help learning R, and > links to other R packages relevant to neuroimaging applications. > > Some features that should be of interest include: > * calculation of a large number of graph/vertex/edge measures > (particularly those most common in neuroimaging) > * between-group vertex-wise analysis using the GLM > * implementation of the network-based statistic (NBS) > * bootstrapping & permutation testing > * random graph generation, small-worldness, and global/local/nodal > efficiency > * rich-club calculations > * robustness ("targeted attack" or "random failure") & vulnerability > * a basic GUI to explore your networks (up to 2 groups/subject at a time) > > Please see the NEWS.md file (https://github.com/cwatson/ > brainGraph/blob/master/NEWS.md) for the changelog. > > This remains a work-in-progress, so I am very happy to receive bug > reports, feature requests, general questions asking for help with code, > (constructive) criticism, etc. > Please join the Google Group that I set up for those purposes: > https://groups.google.com/forum/?hl=en#!forum/brainGraph-help > > Chris Watson > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > The information in this e-mail is intended only for the person to whom it > is > addressed. If you believe this e-mail was sent to you in error and the > e-mail > contains patient information, please contact the Partners Compliance > HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to you in > error > but does not contain patient information, please contact the sender and > properly > dispose of the e-mail. > >
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