Bayesian Exponential Smoothing Models with Trend Modifications R package Rlgt version 0.2-3 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the 'rstan' package. Software Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods, rstantools, forecast, truncnorm Imports: rstan (>= 2.26.0), sn LinkingTo: StanHeaders (>= 2.26.0), rstan (>= 2.26.0), BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.2) Suggests: doParallel, foreach, knitr, rmarkdown, Mcomp, RODBC, dplyr, ggplot2 Christoph Bergmeir <christoph.bergmeir@monash.edu> Comprehensive R Archive Network (CRAN) Slawek Smyl [aut], Christoph Bergmeir [aut, cre], Erwin Wibowo [aut], To Wang Ng [aut], Xueying Long [aut], Alexander Dokumentov [aut], Daniel Schmidt [aut], Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R) GPL-3 2025-04-30 application/tgz https://CRAN.R-project.org/package=Rlgt doi:10.32614/CRAN.package.Rlgt