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SPCAvRP  

Sparse Principal Component Analysis via Random Projections (SPCAvRP)
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("SPCAvRP", "0.4")



Attach the package and use:
library("SPCAvRP")
Maintained by
Milana Gataric
[Scholar Profile | Author Map]
First Published: 2017-12-20
Latest Update: 2019-05-03
Description:
Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <doi:10.48550/arXiv.1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.
How to cite:
Milana Gataric (2017). SPCAvRP: Sparse Principal Component Analysis via Random Projections (SPCAvRP). R package version 0.4, https://cran.r-project.org/web/packages/SPCAvRP. Accessed 31 Mar. 2025.
Previous versions and publish date:
0.1 (2017-12-20 11:51), 0.2 (2018-01-16 13:51), 0.3 (2018-07-26 15:40)
Other packages that cited SPCAvRP R package
View SPCAvRP citation profile
Other R packages that SPCAvRP depends, imports, suggests or enhances
Complete documentation for SPCAvRP
Functions, R codes and Examples using the SPCAvRP R package
Some associated functions: SPCAvRP . SPCAvRP_deflation . SPCAvRP_subspace . 
Some associated R codes: SPCAvRP.R . SPCAvRP_deflation.R . SPCAvRP_subspace.R .  Full SPCAvRP package functions and examples
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