Package: Rlgt
Type: Package
Title: Bayesian Exponential Smoothing Models with Trend Modifications
Version: 0.1-2
URL: https://github.com/cbergmeir/Rlgt
Date: 2019-02-11
Authors@R: c(
    person("Slawek", "Smyl", email = "slaweks@hotmail.co.uk", role = "aut"),
    person("Christoph", "Bergmeir", email = "christoph.bergmeir@monash.edu", role = c("aut", "cre")),
    person("Erwin", "Wibowo", email = "rwinwibowo@gmail.com", role = "aut"),
    person("To Wang", "Ng", email = "edwinnglabs@gmail.com", role = "aut"),
    person("Trustees of Columbia University", "", role = c("cph"), comment= "tools/make_cpp.R, R/stanmodels.R"))
Description: 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.
License: GPL-3
Encoding: UTF-8
LazyData: true
ByteCompile: true
Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods, rstantools, forecast
Imports: rstan (>= 2.18.1), sn
LinkingTo: StanHeaders (>= 2.18.0), rstan (>= 2.18.1), BH (>= 1.66.0),
        Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0)
SystemRequirements: GNU make
NeedsCompilation: yes
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Packaged: 2019-02-11 03:26:18 UTC; bergmeir
Author: Slawek Smyl [aut],
  Christoph Bergmeir [aut, cre],
  Erwin Wibowo [aut],
  To Wang Ng [aut],
  Trustees of Columbia University [cph] (tools/make_cpp.R,
    R/stanmodels.R)
Maintainer: Christoph Bergmeir <christoph.bergmeir@monash.edu>
Repository: CRAN
Date/Publication: 2019-02-22 09:40:03 UTC
Built: R 3.4.4; x86_64-w64-mingw32; 2019-04-25 22:52:34 UTC; windows
Archs: i386, x64
