Package: apca 1.0.0
Angga Dwi Mulyanto
apca: Advanced Principal Component Analysis
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:10.1093/comjnl/4.3.265>, Jacobi Algorithm based on Jacobi (1846) <doi:10.1515/crll.1846.30.51>, Arnoldi Iteration based on Arnoldi (1951) <doi:10.1090/qam/42792>, 'NIPALS' based on Wold (1975) <doi:10.1017/S0021900200047604>, Alternating Least Squares (ALS) based on Kolda and Bader (2009) <doi:10.1137/07070111X>, Probabilistic PCA (PPCA) with EM Algorithm based on Tipping and Bishop (1999) <doi:10.1111/1467-9868.00196>, and Generalized Hebbian Algorithm (GHA) based on Sanger (1989) <doi:10.1016/0893-6080(89)90044-0>.
Authors:
apca_1.0.0.tar.gz
apca_1.0.0.tar.gz(r-4.7-any)apca_1.0.0.tar.gz(r-4.6-any)
apca_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
apca/json (API)
NEWS
| # Install 'apca' in R: |
| install.packages('apca', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:f527c0464d. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 95 | ||
| source / vignettes | OK | 154 | ||
| linux-release-x86_64 | OK | 99 | ||
| wasm-release | OK | 87 |
Exports:apca
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Advanced Principal Component Analysis (APCA) | apca |
| Plot Method for APCA Objects | plot.apca |
| Summary Method for APCA Objects | summary.apca |