{ "_id": "6a2421cca1338011a79f0ac3", "Package": "NetVAR", "Type": "Package", "Title": "Network Structures in VAR Models", "Version": "0.1-2", "Date": "2025-08-26", "Authors@R": "person(given = \"Simon\",\nfamily = \"Trimborn\",\nrole = c(\"aut\", \"cre\"),\nemail = \"trimborn.econometrics@gmail.com\")", "LazyLoad": "yes", "LazyData": "true", "Description": "Vector AutoRegressive (VAR) type models with tailored\nregularisation structures are provided to uncover network type\nstructures in the data, such as influential time series\n(influencers). Currently the package implements the LISAR model\nfrom Zhang and Trimborn (2023) . The\npackage automatically derives the required regularisation\nsequences and refines it during the estimation to provide the\noptimal model. The package allows for model optimisation under\nvarious loss functions such as Mean Squared Forecasting Error\n(MSFE), Akaike Information Criterion (AIC), and Bayesian\nInformation Criterion (BIC). It provides a dedicated class,\nallowing for summary prints of the optimal model and a plotting\nfunction to conveniently analyse the optimal model via\nheatmaps.", "License": "GPL (>=3)", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-06 13:30:21 UTC", "User": "root" }, "RoxygenNote": "7.3.2", "Repository": "https://simontrimborn.r-universe.dev", "Date/Publication": "2026-06-06 12:30:03 UTC", "RemoteUrl": "https://github.com/simontrimborn/netvar", "RemoteRef": "HEAD", "RemoteSha": "13f003e686b5f89a58aff5b81abcadcadbf06b2e", "Author": "Simon Trimborn [aut, cre]", "Maintainer": "Simon Trimborn ", "MD5sum": "1213de722bc31d74c0a9bbb13f3a0107", "_user": "simontrimborn", "_type": "src", "_file": "NetVAR_0.1-2.tar.gz", "_fileid": "3390e5a03f0c3829053197c002d2d0cc7a6f63223622a6d4ac8eab7362266a03", "_filesize": 117548, "_sha256": "3390e5a03f0c3829053197c002d2d0cc7a6f63223622a6d4ac8eab7362266a03", "_created": "2026-06-06T13:30:21.000Z", "_published": "2026-06-06T13:34:04.752Z", 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