GNAR: Methods for Fitting Network Time Series Models

Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables. Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>. Diagnostic tools for GNAR(X) models can be found in Nason et al (2023) <doi:10.48550/arXiv.2312.00530>.

Version: 1.1.3
Depends: R (≥ 3.5.0)
Imports: ggforce, ggplot2, ggpubr, grid, igraph, matrixcalc, rlang, stats, viridis, wordcloud
Published: 2023-12-18
Author: Kathryn Leeming [aut], Guy Nason [aut], Matt Nunes [aut, cre], Marina Knight [ctb], James Wei [aut], Daniel Salnikov [aut], Mario Cortina Borja [ctb]
Maintainer: Matt Nunes <nunesrpackages at gmail.com>
License: GPL-2
NeedsCompilation: no
Citation: GNAR citation info
In views: TimeSeries
CRAN checks: GNAR results

Documentation:

Reference manual: GNAR.pdf

Downloads:

Package source: GNAR_1.1.3.tar.gz
Windows binaries: r-devel: GNAR_1.1.3.zip, r-release: GNAR_1.1.3.zip, r-oldrel: GNAR_1.1.3.zip
macOS binaries: r-release (arm64): GNAR_1.1.3.tgz, r-oldrel (arm64): GNAR_1.1.3.tgz, r-release (x86_64): GNAR_1.1.3.tgz
Old sources: GNAR archive

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