Structural Pursuit Over Multiple Undirected Graphs
Yunzhang Zhu, Xiaotong Shen, Wei Pan, Yiwen Sun
Implement algorithms to recover multiple networks by pursuit of both sparseness and cluster.

An aggressive dimensionality reduction and network estimation
technique for a high-dimensional Gaussian graphical model (GGM). Please
refer to: Efficient Dimensionality Reduction for High-Dimensional Network
Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of
The 31st International Conference on Machine Learning, 2014, p. 1953--1961.

Multivariate Response Generalized Linear Models
Yiwen Zhang <yzhang31@ncsu.edu> and Hua Zhou <hua_zhou@ncsu.edu>
The package provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.

methods, stats, parallel, ggplot2, plyr, reshape2

Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.

Yiwen Zhang <zhangyiwen1015@gmail.com> and Hua Zhou <huazhou@ucla.edu>