Generate High-Dimensional Binary Data with Correlation
Structures
Shuang Song [aut, cre], Wei Jiang [aut], Lin Hou [aut] and Hongyu Zhao [aut]
We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S.,  Hou, L. and Zhao, H. "CorBin: An efficient R package to generate high-dimensional binary data with correlation structures." Submitted to Journal of Statistical Software.

We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S.,  Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with correlation structures." Submitted.

Wei Jiang [aut], Shuang Song [aut, cre], Lin Hou [aut] and Hongyu Zhao [aut]
We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S.,  Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method.

Set-Based Differential Covariance Testing for Genomics
We describe four different summary statistics, to ensure power and flexibility under various settings, including a new connectivity statistic that is sensitive to changes in overall covariance magnitude.