Advanced Methods for Principal Component Analysis and Principal Component Regression
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK11 OK2026-06-01
- ERROR6 OK, 2 ERROR2026-05-30
Documentation
- Help pages
- 8
- Examples that run
- 100%8 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing pcreg adds 27 packages (12 with compiled code, 21 MB of source tarballs). Heaviest: ggplot2 6.0 MB, robustbase 2.2 MB, Rcpp 1.8 MB.
Code & Tests
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- 0.1.1Latest
- 0.1.02026-05-29
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-05-29
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 12 KB
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