CRAN/E | RSpectra

RSpectra

Solvers for Large-Scale Eigenvalue and SVD Problems

Installation

About

R interface to the 'Spectra' library for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user.

github.com/yixuan/RSpectra
Bug report File report

Key Metrics

Version 0.16-1
R ≥ 3.0.2
Published 2022-04-24 723 days ago
Needs compilation? yes
License MPL (≥ 2)
CRAN checks RSpectra results

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Maintainer

Maintainer

Yixuan Qiu

yixuan.qiu@cos.name

Authors

Yixuan Qiu

aut / cre

Jiali Mei

aut

(Function interface of matrix operation)

Gael Guennebaud

ctb

(Eigenvalue solvers from the 'Eigen' library)

Jitse Niesen

ctb

(Eigenvalue solvers from the 'Eigen' library)

Material

README
NEWS
Reference manual
Package source

In Views

NumericalMathematics

Vignettes

Large-Scale Eigenvalue Decomposition and SVD with RSpectra

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

RSpectra archive

Depends

R ≥ 3.0.2

Imports

Matrix ≥ 1.1-0
Rcpp ≥ 0.11.5

Suggests

knitr
rmarkdown
prettydoc

LinkingTo

Rcpp
RcppEigen ≥ 0.3.3.3.0

Reverse Depends

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Reverse Imports

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Reverse LinkingTo

autoFRK
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Gmedian
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RUVIIIC