Packaged: 2017-02-23 01:30:41 UTC; jiabochao

Title: Estimate Directed and Undirected Graphical Models and Construct
Imports: igraph, huge, XMRF, ZIM, mvtnorm, speedglm
Description: Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to Liang, F., Song, Q. and Qiu, P. (2015) <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data, for jointly estimating multiple Gaussian Graphical Models and constructing directed acyclic graph (Bayesian Network).

Title: Estimate Graphical Models from Multiple Types of Datasets and
Description: Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data.
Packaged: 2017-06-07 15:24:19 UTC; jiabochao

Packaged: 2017-09-29 18:49:00 UTC; jiabochao

Packaged: 2018-01-10 18:36:24 UTC; jiabochao

Title: Learning High-Dimensional Graphical Models
Imports: igraph, huge, XMRF, ZIM, mvtnorm, speedglm, SIS, ncvreg,
Description: Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to Liang, F., Song, Q. and Qiu, P. (2015) <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data, jointly estimating multiple Gaussian Graphical Models, constructing single graphical model for heterogeneous dataset, inferring graphical models from high-dimensional missing data and estimating moral graph for Bayesian network.