Title: Independent Component Analysis for Grouped Data
Description: Contains an implementation of an independent component analysis (ICA) for grouped data. The main function groupICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding for user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/groupICA/>.

Title: Regularized Multivariate Regression for Identifying Master
Predictors Using the GroupRemMap Penalty
Author: Xianlong Wang <xwan2@fhcrc.org>, Li Qin, Hexin Zhang, Yuzheng Zhang, Li Hsu, Pei Wang <pei.wang@mssm.edu>
Description: An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.

Title: Simulating and Modeling Group (Pooled) Testing Data
Maintainer: Md S. Warasi <msarker@radford.edu>
Description: Provides an expectation-maximization (EM) algorithm using the approach introduced in Xie (2001) <doi:10.1002/sim.817>. The EM algorithm can be used to estimate the prevalence (overall proportion) of a disease and to estimate a binary regression model from among the class of generalized linear models based on group testing data. The estimation framework we consider offers a flexible and general approach; i.e., its application is not limited to any specific group testing protocol. Consequently, the EM algorithm can model data arising from simple pooling as well as advanced pooling such as hierarchical testing, array testing, and quality control pooling. Also, provided are functions that can be used to conduct the Wald tests described in Buse (1982) <doi:10.1080/00031305.1982.10482817> and to simulate the group testing data described in Kim et al. (2007) <doi:10.1111/j.1541-0420.2007.00817.x>.