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hdpca  

Principal Component Analysis in High-Dimensional Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("hdpca", "1.1.5")



Attach the package and use:
library("hdpca")
Maintained by
Rounak Dey
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-08-02
Latest Update: 2021-01-13
Description:
In high-dimensional settings: Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model. Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) .
How to cite:
Rounak Dey (2016). hdpca: Principal Component Analysis in High-Dimensional Data. R package version 1.1.5, https://cran.r-project.org/web/packages/hdpca. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2016-08-02 09:13), 1.1.3 (2019-10-23 16:30)
Other packages that cited hdpca R package
View hdpca citation profile
Other R packages that hdpca depends, imports, suggests or enhances
Complete documentation for hdpca
Functions, R codes and Examples using the hdpca R package
Some associated functions: hdpc_est . pc_adjust . select.nspike . 
Some associated R codes: pca_functions.R .  Full hdpca package functions and examples
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