An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.

Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.

Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by functions were computed by Momar Dieng's MATLAB package "RMLab". This package is part of a collaboration between Iain Johnstone, Zongming Ma, Patrick Perry, and Morteza Shahram.

Provides two general frameworks to generate a multi-layer network. This also provides several methods to reveal the embedding of both nodes and layers. The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) <arXiv:2302.04437>.

Some basic features of 'MUMPS' (Multifrontal Massively Parallel
         sparse direct Solver) are wrapped in a class whose methods can be used
         for sequentially solving a sparse linear system (symmetric or not)
         with one or many right hand sides (dense or sparse).
         There is a possibility to do separately symbolic analysis,
         LU (or LDL^t) factorization and system solving.
         Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'.
         'MUMPS' method was first described in Amestoy et al. (2001) <doi:10.1137/S0895479899358194>
         and Amestoy et al. (2006) <doi:10.1016/j.parco.2005.07.004>.