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Rssa: A Collection of Methods for Singular Spectrum Analysis

Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See 'citation("Rssa")' for details.

Version: 1.0.5
Depends: R (≥ 3.1), svd (≥ 0.4), forecast
Imports: lattice, methods
Suggests: testthat (≥ 0.7), RSpectra, PRIMME
Published: 2022-08-22
Author: Anton Korobeynikov, Alex Shlemov, Konstantin Usevich, Nina Golyandina
Maintainer: Anton Korobeynikov <anton at korobeynikov.info>
BugReports: https://github.com/asl/rssa/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/asl/rssa
NeedsCompilation: yes
SystemRequirements: fftw (>=3.2)
Citation: Rssa citation info
In views: TimeSeries
CRAN checks: Rssa results

Documentation:

Reference manual: Rssa.pdf

Downloads:

Package source: Rssa_1.0.5.tar.gz
Windows binaries: r-devel: Rssa_1.0.5.zip, r-release: Rssa_1.0.5.zip, r-oldrel: Rssa_1.0.5.zip
macOS binaries: r-release (arm64): Rssa_1.0.5.tgz, r-oldrel (arm64): Rssa_1.0.5.tgz, r-release (x86_64): Rssa_1.0.5.tgz, r-oldrel (x86_64): Rssa_1.0.5.tgz
Old sources: Rssa archive

Reverse dependencies:

Reverse imports: msltrend, Rfssa, TrendSLR, VisitorCounts
Reverse suggests: DecomposeR

Linking:

Please use the canonical form https://CRAN.R-project.org/package=Rssa to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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