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cff-version: 1.2.0
message: 'To cite package "Rlgt" in publications use:'
type: software
license: GPL-3.0-only
title: 'Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications'
version: 0.2-2
doi: 10.32614/CRAN.package.Rlgt
abstract: 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.
authors:
- family-names: Smyl
  given-names: Slawek
  email: slaweks@hotmail.co.uk
- family-names: Bergmeir
  given-names: Christoph
  email: christoph.bergmeir@monash.edu
- family-names: Wibowo
  given-names: Erwin
  email: rwinwibowo@gmail.com
- family-names: Ng
  given-names: To Wang
  email: edwinnglabs@gmail.com
- family-names: Long
  given-names: Xueying
  email: xueying.long@monash.edu
- family-names: Dokumentov
  given-names: Alexander
  email: alexander.dokumentov@gmail.com
- family-names: Schmidt
  given-names: Daniel
  email: daniel.schmidt@monash.edu
repository: https://CRAN.R-project.org/package=Rlgt
repository-code: https://github.com/cbergmeir/Rlgt
url: https://github.com/cbergmeir/Rlgt
date-released: '2024-07-16'
contact:
- family-names: Bergmeir
  given-names: Christoph
  email: christoph.bergmeir@monash.edu