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).
Package details |
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Author | Neo Christopher Chung <nchchung@gmail.com>, John D. Storey <jstorey@princeton.edu> |
Maintainer | Neo Christopher Chung <nchchung@gmail.com> |
License | GPL-2 |
Version | 0.1 |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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