Package: randcorr 1.0
Daniel F. Schmidt
randcorr: Generate a Random p x p Correlation Matrix
Implements the algorithm by Pourahmadi and Wang (2015) <doi:10.1016/j.spl.2015.06.015> 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 pdf proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in Enes Makalic and Daniel F. Schmidt (2018) <arxiv:1809.05212>.
Authors:
randcorr_1.0.tar.gz
randcorr_1.0.tar.gz(r-4.5-noble)randcorr_1.0.tar.gz(r-4.4-noble)
randcorr_1.0.tgz(r-4.4-emscripten)randcorr_1.0.tgz(r-4.3-emscripten)
randcorr.pdf |randcorr.html✨
randcorr/json (API)
# Install 'randcorr' in R: |
install.packages('randcorr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:9c44d079c2. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 23 2024 |
R-4.5-linux | OK | Dec 23 2024 |
Exports:randcorrrandcorr.sample.sink
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
The randcorr package | randcorr-package |
Generate a random p x p correlation matrix | randcorr |
Sample from the (unnormalized) distribution sin(x)^k, 0 < x < pi, k >= 1 | randcorr.sample.sink |