Work

apca: Advanced Principal Component Analysis

Year: 2026

Type: dataset

Abstract: Provides nine computational algorithms for dimensionality reduction via Principal Component Analysis (PCA), built using an object-oriented (S3) architecture.The package includes classical and modern methods: Singular Value Decomposition (SVD) based on Eckart and Young (1936) <doi:10.1007/BF02288367>,Power Iteration based on Hotelling (1933) <doi:10.1037/h0071325>,QR Algorithm based on Francis (1961) <doi:10.

DOI: 10.32614/cran.package.apca

Authors: Angga Dwi Mulyanto, Bambang Widjanarko Otok, Jerry Dwi Trijoyo Purnomo

Institutions: Institut Teknologi Sepuluh Nopember, Universitas Islam Negeri Maulana Malik Ibrahim

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W7159673227

Cites: 9

Cited by:

Related to: 10