A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using  the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.

A set of tools for performing graph theory analysis of brain MRI
    data. It works with data from a Freesurfer analysis (cortical thickness,
    volumes, local gyrification index, surface area), diffusion tensor
    tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from
    DPABI). It contains a graphical user interface for graph visualization and
    data exploration, along with several functions for generating useful
    figures.

It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.

Non-parametric hypothesis testing for populations of brain
    networks represented as graphs, following the L1-distance ANOVA
    framework of Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-21688-0>.
    The package builds on this nonparametric graph-comparison framework,
    extending it with procedures for edge-level inference and identification
    of the specific connections driving group differences. In particular, it
    provides utilities to compute central (mean) graphs, pairwise Manhattan
    distances between adjacency matrices, the group test statistic T, and a
    fast permutation procedure to identify the critical edges that drive
    between-group differences. Helper functions to generate synthetic
    community-structured graphs and to visualise brain networks with
    communities are also included.