Package: TSsmoothing 0.1.0

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>.

Authors:L. Leticia Ramirez-Ramirez [aut, cre], Alejandro Islas-Camargo [aut], Victor M. Guerrero [aut]

TSsmoothing_0.1.0.tar.gz
TSsmoothing_0.1.0.zip(r-4.7-any)TSsmoothing_0.1.0.zip(r-4.6-any)TSsmoothing_0.1.0.zip(r-4.5-any)
TSsmoothing_0.1.0.tgz(r-4.6-any)TSsmoothing_0.1.0.tgz(r-4.5-any)
TSsmoothing_0.1.0.tar.gz(r-4.7-any)TSsmoothing_0.1.0.tar.gz(r-4.6-any)
TSsmoothing_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
TSsmoothing/json (API)

# Install 'TSsmoothing' in R:
install.packages('TSsmoothing', repos = c('https://leticiaram.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • emp_agr - Employment in agriculture
  • ltable - Lambda values table.
  • trade - Annual Trade for USA and Mexico

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 210 downloads 10 exports 21 dependencies

Last updated from:0d5315c52d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK139
source / vignettesOK145
linux-releaseOK112
macos-releaseOK208
macos-oldrelOK193
windows-develOK89
windows-releaseOK87
windows-oldrelOK92
wasm-releaseOK79

Exports:corrmvcgraph_trendlambda_valueplot_trendpositive_definitepreliminarpsigma_estimatessigma_zfsmoothing_leveltrend_estimate

Dependencies:clicpp11farverggplot2gluegridExtragtableisobandlabelinglatticelifecycleMASSMatrixR6RColorBrewerrlangS7scalesvctrsviridisLitewithr