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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: | 0.1.1 |
Depends: | tsBSS (≥ 0.5.3), ICtest (≥ 0.3-4), JADE (≥ 2.0-2), BSSprep, ggplot2 |
Imports: | xts, zoo |
Published: | 2022-12-01 |
DOI: | 10.32614/CRAN.package.ssaBSS |
Author: | Markus Matilainen |
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 |
Reference manual: | ssaBSS.pdf |
Package source: | ssaBSS_0.1.1.tar.gz |
Windows binaries: | r-devel: ssaBSS_0.1.1.zip, r-release: ssaBSS_0.1.1.zip, r-oldrel: ssaBSS_0.1.1.zip |
macOS binaries: | r-release (arm64): ssaBSS_0.1.1.tgz, r-oldrel (arm64): ssaBSS_0.1.1.tgz, r-release (x86_64): ssaBSS_0.1.1.tgz, r-oldrel (x86_64): ssaBSS_0.1.1.tgz |
Old sources: | ssaBSS archive |
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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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