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EESPCA  

Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
View on CRAN: Click here


Download and install EESPCA package within the R console
Install from CRAN:
install.packages("EESPCA")

Install from Github:
library("remotes")
install_github("cran/EESPCA")

Install by package version:
library("remotes")
install_version("EESPCA", "0.7.0")



Attach the package and use:
library("EESPCA")
Maintained by
H. Robert Frost
[Scholar Profile | Author Map]
First Published: 2021-07-16
Latest Update: 2022-06-15
Description:
Contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. Includes logic to support execution of the TPower and rifle sparse PCA methods, as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle via cross-validation to minimize the out-of-sample reconstruction error. H. Robert Frost (2021) .
How to cite:
H. Robert Frost (2021). EESPCA: Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA). R package version 0.7.0, https://cran.r-project.org/web/packages/EESPCA. Accessed 07 May. 2025.
Previous versions and publish date:
0.3.0 (2021-07-16 11:20), 0.4.0 (2021-10-06 16:10), 0.5.0 (2021-10-14 00:00), 0.6.0 (2022-05-16 17:10)
Other packages that cited EESPCA R package
View EESPCA citation profile
Other R packages that EESPCA depends, imports, suggests or enhances
Complete documentation for EESPCA
Functions, R codes and Examples using the EESPCA R package
Some associated functions: ComputeApproxNormSquaredEigenvector . ComputeResidualMatrix . EESPCA-package . EESPCA . EESPCACV . EESPCAForK . PowerIteration . Reconstruct . ReconstructionError . RifleInit . RiflePCACV . Tpower . TpowerPCACV . 
Some associated R codes: EESPCA.R . OtherSPCAMethods.R .  Full EESPCA package functions and examples
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