Package: bootSVD 1.1
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.
Authors:
bootSVD_1.1.tar.gz
bootSVD_1.1.zip(r-4.5)bootSVD_1.1.zip(r-4.4)bootSVD_1.1.zip(r-4.3)
bootSVD_1.1.tgz(r-4.4-any)bootSVD_1.1.tgz(r-4.3-any)
bootSVD_1.1.tar.gz(r-4.5-noble)bootSVD_1.1.tar.gz(r-4.4-noble)
bootSVD_1.1.tgz(r-4.4-emscripten)bootSVD_1.1.tgz(r-4.3-emscripten)
bootSVD.pdf |bootSVD.html✨
bootSVD/json (API)
| # Install 'bootSVD' in R: |
| install.packages('bootSVD', repos = c('https://aaronjfisher.r-universe.dev', 'https://cloud.r-project.org')) |
- EEG_leadingV - Leading 5 Principal Components (PCs) from EEG dataset
- EEG_mu - Functional mean from EEG dataset
- EEG_score_var - Empirical variance of the first 5 score variables from EEG dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:a5dfc1e435. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 08 2024 |
| R-4.5-win | NOTE | Nov 08 2024 |
| R-4.5-linux | NOTE | Nov 08 2024 |
| R-4.4-win | NOTE | Nov 08 2024 |
| R-4.4-mac | NOTE | Nov 08 2024 |
| R-4.3-win | OK | Nov 08 2024 |
| R-4.3-mac | OK | Nov 08 2024 |
Exports:As2VsbootPCAbootSVDbootSVD_LDfastSVDffmatrixmultgenBootIndecesgenQgetMomentsAndMomentCIosqrSVDreindexMatricesByKreindexVectorsByKsimEEG
