Package: onlinePCA 1.3.2
David Degras
onlinePCA: Online Principal Component Analysis
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Authors:
onlinePCA_1.3.2.tar.gz
onlinePCA_1.3.2.tar.gz(r-4.5-noble)onlinePCA_1.3.2.tar.gz(r-4.4-noble)
onlinePCA_1.3.2.tgz(r-4.4-emscripten)onlinePCA_1.3.2.tgz(r-4.3-emscripten)
onlinePCA.pdf |onlinePCA.html✨
onlinePCA/json (API)
NEWS
# Install 'onlinePCA' in R: |
install.packages('onlinePCA', 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 1 years agofrom:4e3bc60f8b. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 10 2024 |
R-4.5-linux-x86_64 | OK | Dec 10 2024 |
Exports:batchpcabsoipcaccipcacoef2fdcreate.basisfd2coefghapcaimputeincRpcaincRpca.blockincRpca.rcperturbationRpcasecularRpcasgapcasnlpcaupdateCovarianceupdateMean
Dependencies:latticeMatrixRcppRcppArmadilloRcppEigenRSpectra
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Online Principal Component Analysis | onlinePCA-package |
Batch PCA | batchpca |
Block Stochastic Orthononal Iteration (BSOI) | bsoipca |
Candid Covariance-Free Incremental PCA | ccipca |
Recover functional data from their B-spline coefficients | coef2fd |
Create a smooth B-spline basis | create.basis |
Compute the coefficients of functional data in a B-spline basis | fd2coef |
Generalized Hebbian Algorithm for PCA | ghapca |
BLUP Imputation of Missing Values | impute |
Incremental PCA | incRpca |
Incremental PCA with Block Update | incRpca.block |
Incremental PCA With Reduced Complexity | incRpca.rc |
Recursive PCA using a rank 1 perturbation method | perturbationRpca |
Recursive PCA Using Secular Equations | secularRpca |
Stochastic Gradient Ascent PCA | sgapca |
Subspace Network Learning PCA | snlpca |
Update the Sample Covariance Matrix | updateCovariance |
Update the Sample Mean Vector | updateMean |