person("John M.", "Marshall", email = "john.marshall@berkeley.edu", role = "aut"),
person("Agastya", "Mondal", email= "agastya_mondal@berkeley.edu", role = "aut")
Suggests: MGDrivE, knitr, rmarkdown, markdown, ggplot2
Packaged: 2022-08-20 01:10:08 UTC; agastyamondal

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

Description: 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.
Packaged: 2014-11-03 08:43:38 UTC; Administrator

Packaged: 2014-11-05 08:31:11 UTC; Administrator

Title: Multivariate Response Generalized Linear Models
Author: Yiwen Zhang <yzhang31@ncsu.edu> and Hua Zhou <hua_zhou@ncsu.edu>
Depends: R (>= 1.15.0), methods, stats, parallel
Description: 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.

Depends: R (>= 2.15.0), methods, stats, parallel

Imports: methods, stats, parallel, ggplot2, plyr, reshape2

Description: 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.