GNU General Public License (GPL) Yes Florey Institute of Neuroscience and Mental Health NITRC A novel group-fused sparse partial correlation method for simultaneous estimation of functional networks in group comparisons Group-fused multiple-graphical lasso cobined with stability selection (GMGLASS) is a software toolbox that can be employed to simultaneously estimate both individual- and group-level functional networks from 2 groups. The software: (1) Reads time series from 2 groups of subjects; (2) Randomly subsamples data 100 times to estimate stability with stability selection; (3) Group-fused multiple-graphical lasso is applied with two hyper-parameters: alpha and beta; (4) Appropriate ranges of hyper-parameters are chosen for achieving stability selection; (5) Both individual- and group-level functional networks can be estimated. The method is described in the following paper: Xiaoyun Liang, David N. Vaughan, Alan Connelly, Fernando Calamante. A novel group-fused sparse partial correlation method for simultaneous estimation of functional networks.in group comparison studies. Brain Topography, 12/2017; DOI:10.1007/s10548-017-0615-6. 2018-1-10 GMGLASS1.01 2018-1-09 GMGLASS1.0 A novel group-fused sparse partial correlation method for simultaneous estimation of functional networks in group comparisons MR, GNU General Public License (GPL) http://www.nitrc.org/projects/gmglass/