{ "_id": "6a1ee155b401979e734109b0", "Package": "starvars", "Type": "Package", "Title": "Vector Logistic Smooth Transition Models Estimation and\nPrediction", "Version": "1.1.10", "Authors@R": "c(\nperson(\"Andrea\", \"Bucci\", email = \"andrea.bucci@unich.it\", role = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Giulio\", \"Palomba\", role = \"aut\"),\nperson(\"Eduardo\", \"Rossi\", role = \"aut\"),\nperson(\"Andrea\", \"Faragalli\", role = \"ctb\")\n)", "Description": "Allows the user to estimate a vector logistic smooth\ntransition autoregressive model via maximum log-likelihood or\nnonlinear least squares. It further permits to test for\nlinearity in the multivariate framework against a vector\nlogistic smooth transition autoregressive model with a single\ntransition variable. The estimation method is discussed in\nTerasvirta and Yang (2014,\n). Also, realized\ncovariances can be constructed from stock market prices or\nreturns, as explained in Andersen et al. (2001,\n).", "License": "GPL", "Encoding": "UTF-8", "LazyData": "true", "URL": "https://github.com/andbucci/starvars", "RoxygenNote": "7.1.1", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev make libharfbuzz-dev libicu-dev libjpeg-dev\nlibpng-dev libuv1-dev libxml2-dev libssl-dev zlib1g-dev", "Repository": "https://andbucci.r-universe.dev", "Date/Publication": "2022-01-17 17:03:29 UTC", "RemoteUrl": "https://github.com/andbucci/starvars", "RemoteRef": "HEAD", "RemoteSha": "c4b8fcb3bc06718f1109699bfbf2c408ff0617c0", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-20 07:02:54 UTC", "User": "root" }, "Author": "Andrea Bucci [aut, cre, cph],\nGiulio Palomba [aut],\nEduardo Rossi [aut],\nAndrea Faragalli [ctb]", "Maintainer": "Andrea Bucci ", "MD5sum": "d263d39ea5ec2ce08e18938915172123", "_user": "andbucci", "_type": "src", "_file": "starvars_1.1.10.tar.gz", "_fileid": "1179f3a325f4ce36a823e65df891b5e9b8b6c3455c6a7cbebd6dff6681e6dd2a", "_filesize": 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