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ddpca  

Diagonally Dominant Principal Component Analysis
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("ddpca", "1.1")



Attach the package and use:
library("ddpca")
Maintained by
Fan Yang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-07-19
Latest Update: 2019-09-14
Description:
Efficient procedures for fitting the DD-PCA (Ke et al., 2019, ) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.
How to cite:
Fan Yang (2019). ddpca: Diagonally Dominant Principal Component Analysis. R package version 1.1, https://cran.r-project.org/web/packages/ddpca. Accessed 27 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:31), 1.0 (2019-07-19 10:30)
Other packages that cited ddpca R package
View ddpca citation profile
Other R packages that ddpca depends, imports, suggests or enhances
Complete documentation for ddpca
Functions, R codes and Examples using the ddpca R package
Some associated functions: DDHC . DDPCA_convex . DDPCA_nonconvex . HCdetection . IHCDD . ProjDD . ProjSDD . ddpca-package . 
Some associated R codes: DDHC.R . DDPCA_convex.R . DDPCA_nonconvex.R . HCdetection.R . IHCDD.R . ProjDD.R . ProjSDD.R .  Full ddpca package functions and examples
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