Forecasting Using State Space Models R package smooth version 4.5.0 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi:10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi:10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi:10.1080/00207543.2017.1380326>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi:10.13140/RG.2.2.35897.06242>). Software Depends: R (>= 3.0.2), greybox (>= 2.0.2) Imports: Rcpp (>= 0.12.3), stats, generics (>= 0.1.2), graphics, grDevices, methods, statmod, MASS, nloptr, utils, xtable, zoo LinkingTo: Rcpp, RcppArmadillo (>= 0.8.100.0.0) Suggests: legion, numDeriv, testthat, knitr, rmarkdown, doMC, doParallel, foreach Ivan Svetunkov <ivan@svetunkov.com> Comprehensive R Archive Network (CRAN) Ivan Svetunkov [aut, cre] (Senior Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK) LGPL-2.1 2026-06-21 application/tgz https://CRAN.R-project.org/package=smooth doi:10.32614/CRAN.package.smooth en-GB