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TSsmoothing: Trend Estimation of Univariate and Bivariate Time Series with Controlled Smoothness

It performs the smoothing approach provided by penalized least squares for univariate and bivariate time series, as proposed by Guerrero (2007) and Gerrero et al. (2017). This allows to estimate the time series trend by controlling the amount of resulting (joint) smoothness. — Guerrero, V.M (2007) <doi:10.1016/j.spl.2007.03.006>. Guerrero, V.M; Islas-Camargo, A. and Ramirez-Ramirez, L.L. (2017) <doi:10.1080/03610926.2015.1133826>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: ggplot2 (≥ 3.2.0), MASS (≥ 7.3.0), gridExtra (≥ 2.3.0), Matrix (≥ 1.2.0)
Published: 2019-07-15
DOI: 10.32614/CRAN.package.TSsmoothing
Author: L. Leticia Ramirez-Ramirez [aut, cre], Alejandro Islas-Camargo [aut], Victor M. Guerrero [aut]
Maintainer: L. Leticia Ramirez-Ramirez <leticia.ramirez at cimat.mx>
License: GPL-3
NeedsCompilation: no
CRAN checks: TSsmoothing results

Documentation:

Reference manual: TSsmoothing.pdf

Downloads:

Package source: TSsmoothing_0.1.0.tar.gz
Windows binaries: r-devel: TSsmoothing_0.1.0.zip, r-release: TSsmoothing_0.1.0.zip, r-oldrel: TSsmoothing_0.1.0.zip
macOS binaries: r-release (arm64): TSsmoothing_0.1.0.tgz, r-oldrel (arm64): TSsmoothing_0.1.0.tgz, r-release (x86_64): TSsmoothing_0.1.0.tgz, r-oldrel (x86_64): TSsmoothing_0.1.0.tgz

Linking:

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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.