This package contains a function to generate a random p x p correlation matrix. This function implements the algorithm by Pourahmadi and Wang  for generating a random p x p correlation matrix. Briefly, the idea is to represent the correlation matrix using Cholesky factorization and p(p-1)/2 hyperspherical coordinates (i.e., angles), sample the angles from a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with a probability density function proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in .
For usage, see the examples in
To cite this package please reference:
Makalic, E. & Schmidt, D. F. An efficient algorithm for sampling from sin^k(x) for generating random correlation matrices arXiv:1809.05212, 2018 https://arxiv.org/abs/1809.05212
A MATLAB-compatible implementation of the sampler in this package can be obtained from:
Daniel Schmidt email@example.com
Faculty of Information Technology, Monash University, Australia
Enes Makalic firstname.lastname@example.org
Centre for Epidemiology and Biostatistics, The University of Melbourne, Australia
 Mohsen Pourahmadi and Xiao Wang, Distribution of random correlation matrices: Hyperspherical parameterization of the Cholesky factor, Statistics & Probability Letters, Volume 106, Pages 5-12, 2015.
 Enes Makalic and Daniel F. Schmidt An efficient algorithm for sampling from sin^k(x) for generating random correlation matrices, arXiv:1809.05212, 2018.
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