Abstract:
The need for statistical tools capable of addressing high-dimensional data is ever-growing. One such tool is that of differential networks, which have become increasing popular within various branches of science. The popularity of differential networks and their subsequent analysis is largely attributed to their ability to effectively represent the relationships between factors of complex systems over time, or over various experimental conditions. However, a differential network is not easily calculated, and in high dimensional settings common within biological sciences they must be estimated.Motivated by this, this dissertation comprehensively explores differential networks and the efficient estimation thereof through the use of a R package developed throughout the course of this research- dineR.
