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.cov, which estimate sparse loadings of principal components
and large-scale covariance matrix, respectively.
|Imports:||corpcor, qvalue, jackstraw, lfa|
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