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pcreg  

Advanced Methods for Principal Component Analysis and Principal Component Regression
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("pcreg", "0.1.1")



Attach the package and use:
library("pcreg")
Maintained by
Dr. Pramit Pandit
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-29
Latest Update: 2026-05-31
Description:
Provides a unified framework for principal component analysis (PCA) and principal component regression (PCR), including standard PCA, sparse PCA, robust PCA, and supervised PCA. The package supports automatic selection of the number of components using cumulative variance and elbow methods and integrates PCA with regression modelling through PCR models. It includes tools for PCA suitability assessment using Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure. Visualisation utilities such as scree plots and biplots are provided for interpretation. The methods are designed to handle multicollinearity, outliers, and high-dimensional data, making them suitable for applied statistical modelling and data analysis. The methodology is based on established approaches described in Jolliffe (2002) <doi:10.1007/b98835>, Zou et al. (2006) <doi:10.1111/j.1467-9868.2005.00503.x>, and Hubert et al. (2005) <doi:10.1198/004017004000000563>.
How to cite:
Dr. Pramit Pandit (2026). pcreg: Advanced Methods for Principal Component Analysis and Principal Component Regression. R package version 0.1.1, https://cran.r-project.org/web/packages/pcreg. Accessed 24 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:40), 0.1.0 (2026-05-29 11:50)
Other packages that cited pcreg R package
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Complete documentation for pcreg
Functions, R codes and Examples using the pcreg R package
Full pcreg package functions and examples
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