jaws-package: Jackstraw Weighted Shrinkage Methods
In ncchung/jaws: Jackstraw Weighted Shrinkage Methods

Estimate sparse loadings (i.e., coefficients) of Principal Component Analysis, Logistic Factor Analysis, and other techniques in the context of Latent Variable Models. Generally, this can facilitate calculation of shrunken R^2 and related quantities that represent estimated latent variables more accurately. Using systematic variation driven by latent variables, this package also estimate covariance matrices of high-dimensional data when a number of rows (variables) is exceedingly larger than a number of observations (columns).

Two main functions are jaws.pca and jaws.cov, which estimate sparse loadings of principal components and large-scale covariance matrix, respectively.