# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "farfima" in publications use:'
type: software
license: GPL-2.0-only
title: 'farfima: Order Selection and Forecasting in Long-Memory Functional Time Series'
version: 0.1.0
abstract: Provides tools for modelling, forecasting and visualising long-memory functional
  time series with the functional autoregressive fractionally integrated moving-average
  model. The model is fitted in three steps, each of which can also be used on its
  own. First, the memory parameter is estimated from the leading principal component
  scores, for example by local Whittle or by bias-corrected local polynomial Whittle
  with noise. Second, the autoregressive and moving-average orders of the fractionally
  differenced curves are selected by a functional information criterion, functional
  Box-Jenkins or cross-validation. Third, the operators are estimated. The point forecasts
  come with conformal prediction bands that quantify their uncertainty. Visualisation
  tools include improved rainbow plots and plots of the fitted model and its forecasts.
authors:
- family-names: Liu
  given-names: Chang
  email: chang.liu45@students.mq.edu.au
- family-names: Shang
  given-names: Han Lin
- family-names: Tarr
  given-names: Garth
- family-names: Müller
  given-names: Samuel
repository: https://cran.r-universe.dev
commit: b943781476b7191401ef0f319832e0010e9251f2
date-released: '2026-11-10'
contact:
- family-names: Liu
  given-names: Chang
  email: chang.liu45@students.mq.edu.au