smooth: Forecasting Using Smoothing Functions

Functions implementing Single Source of Error state-space models for purposes of time series analysis and forecasting. The package includes exponential smoothing, SARIMA in state-space forms and several simulation functions.

Version: 2.4.3
Depends: R (≥ 3.0.2), greybox (≥ 0.2.1)
Imports: Rcpp (≥ 0.12.3), stats, graphics, forecast, nloptr, utils, zoo
LinkingTo: Rcpp, RcppArmadillo (≥ 0.8.100.0.0)
Suggests: Mcomp, numDeriv, testthat, knitr, rmarkdown
Published: 2018-05-03
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)
Maintainer: Ivan Svetunkov <ivan at svetunkov.ru>
BugReports: https://github.com/config-i1/smooth/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/config-i1/smooth
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: smooth results

Downloads:

Reference manual: smooth.pdf
Vignettes: ces() - Complex Exponential Smoothing
es() - Exponential Smoothing
ges() - Generalised Exponential Smoothing
simulate(), sim.es(), sim.ssarima(), sim.ces() - simulate functions for ETS, SARIMA and CES
sma() - Simple Moving Average
smooth
ssarima() - State-Space ARIMA
ves() - Vector Exponential Smoothing
Package source: smooth_2.4.3.tar.gz
Windows binaries: r-devel: smooth_2.4.3.zip, r-release: smooth_2.4.3.zip, r-oldrel: smooth_2.4.3.zip
OS X binaries: r-release: smooth_2.4.3.tgz, r-oldrel: smooth_2.4.1.tgz
Old sources: smooth archive

Reverse dependencies:

Reverse depends: MAPA
Reverse suggests: greybox

Linking:

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