CorBin: Generate High-Dimensional Binary Data with Correlation Structures

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.

Version:	1.0.0
Published:	2020-11-14
DOI:	10.32614/CRAN.package.CorBin
Author:	Wei Jiang [aut], Shuang Song [aut, cre], Lin Hou [aut] and Hongyu Zhao [aut]
Maintainer:	Shuang Song <song-s19 at mails.tsinghua.edu.cn>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	CorBin results

Documentation:

Reference manual:

CorBin.html , CorBin.pdf

Downloads:

Package source:	CorBin_1.0.0.tar.gz
Windows binaries:	r-devel: CorBin_1.0.0.zip, r-release: CorBin_1.0.0.zip, r-oldrel: CorBin_1.0.0.zip
macOS binaries:	r-release (arm64): CorBin_1.0.0.tgz, r-oldrel (arm64): CorBin_1.0.0.tgz, r-release (x86_64): CorBin_1.0.0.tgz, r-oldrel (x86_64): CorBin_1.0.0.tgz
Old sources:	CorBin archive

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