Description: The algorithm pursues the identification of the set of variables driving the differences in two different experimental conditions (i.e., the primary genes) within a graphical model context. It uses the idea of simultaneously looking for the differences between two multivariate normal distributions in all marginal and conditional distributions associated with a decomposable graph, which represents the pathway under exam. The implementation accommodates genomics specific issues (low sample size and multiple testing issues) and provides a number of functions offering numerical and visual summaries to help the user interpret the obtained results. In order to use the (optional) 'Cytoscape' functionalities, the suggested 'r2cytoscape' package must be installed from the 'GitHub' repository ('devtools::install_github('cytoscape/r2cytoscape')'). More details in Salviato et al., (2020) <doi:10.1371/journal.pcbi.1007357> and Djordjilovic et al., (2022) <doi:10.1016/j.jmva.2022.104973>.
Suggests: AnnotationDbi, networkD3, ggplot2, grDevices, Rgraphviz,
knitr, rmarkdown, r2cytoscape, BiocStyle, Biobase, graphite,

Title: Soybean Nested Association Mapping Dataset
Author: Alencar Xavier, William Muir, William Beavis, James Specht, Brian Diers, Katy Rainey.
Description: Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB), pre-formatted for general analysis and genome-wide association analysis using the NAM package.

Author: Alencar Xavier, William Beavis, James Specht, Brian Diers, William Muir, Katy Rainey.

Author: Alencar Xavier, William Beavis, James Specht, Brian Diers, Reka Howard, William Muir, Katy Rainey.

Author: Alencar Xavier, William Beavis, James Specht, Brian Diers, Reka Howard,
Description: Genomic and multi-environmental soybean data. Soybean Nested
Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
(USB), pre-formatted for general analysis and genome-wide association analysis