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
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


Reference manual: TSsmoothing.pdf


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-release (x86_64): TSsmoothing_0.1.0.tgz, r-oldrel: TSsmoothing_0.1.0.tgz


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