onlinePCA (version 1.3.2)

Online Principal Component Analysis

Description

Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.

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Version

Install

install.packages('onlinePCA')

Monthly Downloads

170

Version

1.3.2

License

GPL-3

Maintainer

David Degras

Last Published

November 15th, 2023

Functions in onlinePCA (1.3.2)

updateCovariance

Update the Sample Covariance Matrix

snlpca

Subspace Network Learning PCA

incRpca.rc

Incremental PCA With Reduced Complexity

perturbationRpca

Recursive PCA using a rank 1 perturbation method

sgapca

Stochastic Gradient Ascent PCA

updateMean

Update the Sample Mean Vector

onlinePCA-package

Online Principal Component Analysis

secularRpca

Recursive PCA Using Secular Equations

incRpca

Incremental PCA

ghapca

Generalized Hebbian Algorithm for PCA

fd2coef

Compute the coefficients of functional data in a B-spline basis

bsoipca

Block Stochastic Orthononal Iteration (BSOI)

incRpca.block

Incremental PCA with Block Update

coef2fd

Recover functional data from their B-spline coefficients

ccipca

Candid Covariance-Free Incremental PCA

create.basis

Create a smooth B-spline basis

batchpca

Batch PCA

impute

BLUP Imputation of Missing Values