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).
|Author||Neo Christopher Chung <[email protected]>, John D. Storey <[email protected]>|
|Maintainer||Neo Christopher Chung <[email protected]>|
|Package repository||View on GitHub|
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