Two-Sample Testing in
High-Dimensional Models (Differential Network & Differential Regression)

DiffNet: Network inference is
subject to statistical uncertainty and observed differences
between two networks inferred from two datasets may be due to
noise and variability in estimation rather than any true
difference in underlying network topology. Significance testing
for network differences is a challenging statistical problem,
involving high-dimensional estimation and comparison of
non-nested hypotheses. Our recently developed method
"differential network" performs formal two-sample testing
between high-dimensional Gaussian graphical models (GGMs) and is
implemented in the **R-package
DiffNet**. For technical details of the
approach we refer the reader to: Städler, N. and Mukherjee,
S. (2013). Two-Sample Testing in High-Dimensional
Models. Preprint arXiv:1210.4584. Manual.

DiffRegr: The **R-package** * DiffRegr* performs formal two-sample testing between high-dimensional regression models. For technical details of the approach we refer the reader to: Städler, N. and Mukherjee, S. (2013). Two-Sample Testing in High-Dimensional Models. Preprint arXiv:1210.4584. Manual.