Package: NetVAR 0.1-2

NetVAR: Network Structures in VAR Models

Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.

Authors:Simon Trimborn [aut, cre]

NetVAR_0.1-2.tar.gz
NetVAR_0.1-2.zip(r-4.7-any)NetVAR_0.1-2.zip(r-4.6-any)NetVAR_0.1-2.zip(r-4.5-any)
NetVAR_0.1-2.tgz(r-4.6-any)NetVAR_0.1-2.tgz(r-4.5-any)
NetVAR_0.1-2.tar.gz(r-4.7-any)NetVAR_0.1-2.tar.gz(r-4.6-any)
NetVAR_0.1-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
NetVAR/json (API)

# Install 'NetVAR' in R:
install.packages('NetVAR', repos = c('https://simontrimborn.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/simontrimborn/netvar/issues

Datasets:
  • TradingData - Trading data for 11 US stocks from the finance sector

On CRAN:

Conda:

3.18 score 3 stars 199 downloads 1 exports 22 dependencies

Last updated from:13f003e686. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK129
source / vignettesOK150
linux-release-x86_64OK136
macos-release-arm64OK89
macos-oldrel-arm64OK146
windows-devel-x86_64OK89
windows-release-x86_64OK106
windows-oldrel-x86_64OK106
wasm-releaseOK79

Exports:LISAR

Dependencies:cvardotCall64fastICAfBasicsfGarchfieldsgbutilsgsslatticemapsMASSMatrixrbibutilsRColorBrewerRcppRdpackspamspatialstabledisttimeDatetimeSeriesviridisLite