ssaBSS: Stationary Subspace Analysis

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

Version: 0.1
Depends: tsBSS (≥ 0.5.3), ICtest (≥ 0.3-4), JADE (≥ 2.0-2), BSSprep, ggplot2
Imports: xts, zoo
Published: 2021-04-12
Author: Markus Matilainen ORCID iD [cre, aut], Lea Flumian [aut], Klaus Nordhausen ORCID iD [aut], Sara Taskinen ORCID iD [aut]
Maintainer: Markus Matilainen <markus.matilainen at outlook.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: ssaBSS results

Downloads:

Reference manual: ssaBSS.pdf
Package source: ssaBSS_0.1.tar.gz
Windows binaries: r-devel: ssaBSS_0.1.zip, r-release: ssaBSS_0.1.zip, r-oldrel: ssaBSS_0.1.zip
macOS binaries: r-release (arm64): ssaBSS_0.1.tgz, r-release (x86_64): ssaBSS_0.1.tgz, r-oldrel: ssaBSS_0.1.tgz

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