# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "TrendLSW" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'TrendLSW: Wavelet Methods for Analysing Locally Stationary Time Series'
version: 1.0.2.9000
doi: 10.32614/CRAN.package.TrendLSW
abstract: Fitting models for, and simulation of, trend locally stationary wavelet
  (TLSW) time series models, which take account of time-varying trend and dependence
  structure in a univariate time series. The TLSW model, and its estimation, is described
  in McGonigle, Killick and Nunes (2022a) <https://doi.org/10.1111/jtsa.12643>, (2022b)
  <https://doi.org/10.1214/22-EJS2044>. New users will likely want to start with the
  TLSW function.
authors:
- family-names: McGonigle
  given-names: Euan T.
  email: e.t.mcgonigle@soton.ac.uk
- family-names: Killick
  given-names: Rebecca
- family-names: Nunes
  given-names: Matthew
repository: https://euanmcgonigle.r-universe.dev
repository-code: https://github.com/EuanMcGonigle/TrendLSW
commit: 638f22a1a2481cc0a4032eec1bb409b1dd1357d9
url: https://github.com/EuanMcGonigle/TrendLSW
contact:
- family-names: McGonigle
  given-names: Euan T.
  email: e.t.mcgonigle@soton.ac.uk