# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "NetVAR" in publications use:' type: software title: 'NetVAR: Network Structures in VAR Models' version: 0.1-2 identifiers: - type: doi value: 10.32614/CRAN.package.NetVAR abstract: 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) . 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: - family-names: Trimborn given-names: Simon email: trimborn.econometrics@gmail.com preferred-citation: type: article title: Influential assets in Large-Scale Vector AutoRegressive Models authors: - family-names: Zhang given-names: Kexin - family-names: Trimborn given-names: Simon email: trimborn.econometrics@gmail.com year: '2023' journal: Discussion Paper url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4619531 repository: https://simontrimborn.r-universe.dev commit: 13f003e686b5f89a58aff5b81abcadcadbf06b2e date-released: '2025-08-26' contact: - family-names: Trimborn given-names: Simon email: trimborn.econometrics@gmail.com references: - type: manual title: 'NetVAR: Network structure VAR models' authors: - family-names: Trimborn given-names: Simon year: '2025'