sparsepca: Sparse Principal Component Analysis
Fits principal components with sparse loading vectors for interpretable dimension reduction. Variable projection combines an orthogonal reconstruction factor with elastic net or cardinality regularization of the loadings. Deterministic, randomized, and robust algorithms support exploratory analysis of high-dimensional data; the randomized algorithm also supports sparse input matrices through implicit centering and scaling. A robust formulation separates entrywise outliers from the low-rank reconstruction. Regularization paths and approximate feature-count searches reuse matrix factorizations, and prediction applies the fitted transformation to new observations. The methods are described in Erichson et al. (2020) <doi:10.1137/18M1211350>.
| Version: | 1.0.0 |
| Depends: | R (≥ 4.4.0) |
| Imports: | Matrix (≥ 1.7-0), methods, rsvd (≥ 2.0.0) |
| Suggests: | testthat (≥ 3.3.2) |
| Published: | 2026-11-06 |
| DOI: | 10.32614/CRAN.package.sparsepca |
| Author: | N. Benjamin Erichson [aut, cre],
Peng Zheng [aut],
Sasha Aravkin [aut],
Maria A. Sans-Fuentes
 [ctb]
(Reported the robust objective error) |
| Maintainer: | N. Benjamin Erichson <erichson at icsi.berkeley.edu> |
| BugReports: | https://github.com/erichson/spca/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/erichson/spca |
| NeedsCompilation: | no |
| Citation: | sparsepca citation info |
| Materials: | NEWS |
| CRAN checks: | sparsepca results |
Documentation:
| Reference manual: | sparsepca.html , sparsepca.pdf |
Downloads:
| Package source: | sparsepca_1.0.0.tar.gz |
| Windows binaries: | r-devel: sparsepca_0.1.2.zip, r-release: sparsepca_0.1.2.zip, r-oldrel: sparsepca_1.0.0.zip |
| macOS binaries: | r-release (arm64): sparsepca_1.0.0.tgz, r-oldrel (arm64): sparsepca_1.0.0.tgz, r-release (x86_64): sparsepca_1.0.0.tgz, r-oldrel (x86_64): sparsepca_1.0.0.tgz |
| Old sources: | sparsepca archive |
Reverse dependencies:
| Reverse imports: | categoryEncodings, clusterability, robCompositions, scPCA, SparseBiplots |
| Reverse suggests: | parameters, ptLasso |
Linking:
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