doi:10.1080/01621459.2016.1264956>, compare the performance with linear models, and construct networks with partial derivatives.">

quadVAR: Quadratic Vector Autoregression (original) (raw)

Estimate quadratic vector autoregression models with the strong hierarchy using the Regularization Algorithm under Marginality Principle (RAMP) by Hao et al. (2018) <doi:10.1080/01621459.2016.1264956>, compare the performance with linear models, and construct networks with partial derivatives.

Version: 0.1.2
Imports: cli, dplyr, ggplot2, magrittr, ncvreg, qgraph, RAMP, rlang, shiny, shinythemes, stats, stringr, tibble, tidyr
Suggests: nonlinearTseries, remotes, SIS, testthat (≥ 3.0.0)
Published: 2025-02-11
DOI: 10.32614/CRAN.package.quadVAR
Author: Jingmeng Cui ORCID iD [aut, cre]
Maintainer: Jingmeng Cui <jingmeng.cui at outlook.com>
BugReports: https://github.com/Sciurus365/quadVAR/issues
License: GPL (≥ 3)
URL: https://github.com/Sciurus365/quadVAR,https://sciurus365.github.io/quadVAR/
NeedsCompilation: no
Materials: README, NEWS
In views: TimeSeries
CRAN checks: quadVAR results

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