bootSVD: Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data

Implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in <doi:10.1080/01621459.2015.1062383> (see also <arXiv:1405.0922> ). For data matrices that are too large to operate on in memory, users can input objects with class 'ff' (see the 'ff' package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the 'parallel' package can be used to parallelize the calculation of the bootstrap PCs.

Getting started

Package details

AuthorAaron Fisher <>
MaintainerAaron Fisher <>
Package repositoryView on CRAN
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bootSVD documentation built on Feb. 2, 2021, 5:06 p.m.