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 form a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with probability density function sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in .
 Mohsen Pourahmadi and Xiao Wang, Distribution of random correlation matrices: Hyperspherical parameterization of the Cholesky factor, Statistics & Probability Letters, Volume 106, November 2015, Pages 5-12
 Enes Makalic and Daniel F. Schmidt, An efficient algorithm for sampling from $\sin^k(x)$ for generating random correlation matrices, arxiv, 2018
-Code is now vectorised and is approximately 10x faster than the previous (non-vectorised) version.
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