Package: sparsepca 0.1.0

sparsepca: Sparse Principal Component Analysis (SPCA)

Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data.

Authors:N. Benjamin Erichson, Peng Zheng, and Sasha Aravkin

sparsepca_0.1.0.tar.gz
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sparsepca.pdf |sparsepca.html
sparsepca/json (API)

# Install 'sparsepca' in R:
install.packages('sparsepca', repos = c('https://erichson.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/erichson/spca/issues

On CRAN:

Conda:

dimension-reductiondimensionality-reductionpcaspca

7.28 score 68 stars 3 packages 86 scripts 1.2k downloads 3 mentions 3 exports 3 dependencies

Last updated 7 years agofrom:fa896289a6. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 28 2025
R-4.5-winNOTEMar 28 2025
R-4.5-macNOTEMar 28 2025
R-4.5-linuxNOTEMar 28 2025
R-4.4-winNOTEMar 28 2025
R-4.4-macNOTEMar 28 2025
R-4.4-linuxNOTEMar 28 2025
R-4.3-winNOTEMar 28 2025
R-4.3-macNOTEMar 28 2025

Exports:robspcarspcaspca

Dependencies:latticeMatrixrsvd