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sparsepca

Sparse Principal Component Analysis (SPCA)

v0.1.2 · Apr 11, 2018 · GPL (>= 3)

Description

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. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.

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Maintainer: ‘N. Benjamin Erichson <erichson@uw.edu>’

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  Authors@R: c(person(given = c("N.", "Benjamin"),
                      family = "Erichson",
                      role = c("aut", "cre"),
                      email = "erichson@uw.edu"),
               person(given = "Peng",
                      family = "Zheng",
                      role = "aut"),
               person(given = "Sasha",
                      family = "Aravkin",
                      role = "aut"))
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The Description field contains
  Erichson et al. (2018) <arXiv:1804.00341>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
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Maintainer: ‘N. Benjamin Erichson <erichson@uw.edu>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = c("N.", "Benjamin"),
                      family = "Erichson",
                      role = c("aut", "cre"),
                      email = "erichson@uw.edu"),
               person(given = "Peng",
                      family = "Zheng",
                      role = "aut"),
               person(given = "Sasha",
                      family = "Aravkin",
                      role = "aut"))
as necessary.

The Description field contains
  Erichson et al. (2018) <arXiv:1804.00341>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
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NOTE 9 OK · 5 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘N. Benjamin Erichson <erichson@uw.edu>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = c("N.", "Benjamin"),
                      family = "Erichson",
                      role = c("aut", "cre"),
                      email = "erichson@uw.edu"),
               person(given = "Peng",
                      family = "Zheng",
                      role = "aut"),
               person(given = "Sasha",
                      family = "Aravkin",
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘N. Benjamin Erichson <erichson@uw.edu>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: c(person(given = c("N.", "Benjamin"),
                      family = "Erichson",
                      role = c("aut", "cre"),
                      email = "erichson@uw.edu"),
               person(given = "Peng",
                      family = "Zheng",
                      role = "aut"),
               person(given = "Sasha",
                      family = "Aravkin",
NOTE r-oldrel-macos-arm64

LazyData

  'LazyData' is specified without a 'data' directory
NOTE r-oldrel-macos-x86_64

LazyData

  'LazyData' is specified without a 'data' directory
NOTE r-oldrel-windows-x86_64

LazyData

  'LazyData' is specified without a 'data' directory

Reverse Dependencies (5)

suggests

Dependency Network

Dependencies Reverse dependencies rsvd SparseBiplots categoryEncodings clusterability robCompositions parameters sparsepca

Version History

new 0.1.2 Mar 9, 2026