Authors@R: c(person("Han", "Cao", email=c("hank9cao@gmail.com", "han.cao@zi-mannheim.de"), role=c("cre", "aut", "cph")),
Description: 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>.

Title: Signal-Noise Separation in Random Matrices by using Eigenvalue
Description: 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.

Title: Distributions and Statistics from Random Matrix Theory
Author: Patrick O. Perry <patperry@gmail.com>, with contributions from
Iain M. Johnstone, Zongming Ma, and Morteza Shahram.
Maintainer: Patrick O. Perry <patperry@gmail.com>
Description: Functions for working with the Tracy-Widom laws and other
distributions related to the eigenvalues of large Wishart
The tables for computing the Tracy-Widom densities and distribution
functions were computed by Momar Dieng's MATLAB package
"RMLab", which is available on his homepage at
This package is part of a collaboration between Iain Johnstone,
Zongming Ma, Patrick Perry, and Morteza Shahram.  It will soon
be replaced by a package with more accuracy and built-in