This package contains a function to generate a random p x p correlation matrix. This function implements the algorithm by Pourahmadi and Wang [1] 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 [2].
For usage, see the examples in randcorr and randcorr.sample.sink.
[1] 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.
[2] Enes Makalic and Daniel F. Schmidt An efficient algorithm for sampling from sin^k(x) for generating random correlation matrices, arXiv:1809.05212, 2018.