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TSsmoothing
View on CRAN: Click
here
Download and install TSsmoothing package within the R console
Install from CRAN:
install.packages("TSsmoothing")
Install from Github:
library("remotes")
install_github("cran/TSsmoothing")
Install by package version:
library("remotes")
install_version("TSsmoothing", "0.1.0")
Attach the package and use:
library("TSsmoothing")
Maintained by
L. Leticia Ramirez-Ramirez
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-07-15
Latest Update: 2019-07-15
Description:
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>.
How to cite:
L. Leticia Ramirez-Ramirez (2019). TSsmoothing: Trend Estimation of Univariate and Bivariate Time Series with Controlled Smoothness. R package version 0.1.0, https://cran.r-project.org/web/packages/TSsmoothing. Accessed 22 Dec. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for TSsmoothing
Functions, R codes and Examples using
the TSsmoothing R package
Some associated functions: corrmvc . emp_agr . graph_trend . lambda_value . ltable . plot_trend . positive_definite . preliminar . psigma_estimates . sigma_zf . smoothing_level . trade . trend_estimate .
Some associated R codes: corrmvc.R . data.R . globals.R . graph_trend.R . lambda_value.R . plot_trend.R . positive_definite.R . preliminar.R . psigma_estimates.R . sigma_zf.R . smoothing_level.R . trend_estimate.R . Full TSsmoothing package functions and examples
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