{ "_id": "6a1eef13b401979e73412ae3", "Package": "neighbours", "Type": "Package", "Title": "Neighbourhood Functions for Local-Search Algorithms", "Version": "0.1-5", "Date": "2025-12-17", "Maintainer": "Enrico Schumann ", "Authors@R": "person(given = \"Enrico\", family = \"Schumann\",\nrole = c(\"aut\", \"cre\"),\nemail = \"es@enricoschumann.net\",\ncomment = c(ORCID = \"0000-0001-7601-6576\"))", "Description": "Neighbourhood functions are key components of local-search\nalgorithms such as Simulated Annealing or Threshold Accepting.\nThese functions take a solution and return a slightly-modified\ncopy of it, i.e. a neighbour. The package provides a function\nneighbourfun() that constructs such neighbourhood functions,\nbased on parameters such as admissible ranges for elements in a\nsolution. Supported are numeric and logical solutions. The\nalgorithms were originally created for portfolio-optimisation\napplications, but can be used for other models as well. Several\nrecipes for neighbour computations are taken from \"Numerical\nMethods and Optimization in Finance\" by M. Gilli, D. Maringer\nand E. Schumann (2019, ISBN:978-0128150658).", "License": "GPL-3", "URL": "https://enricoschumann.net/R/packages/neighbours/ ,\nhttps://sr.ht/~enricoschumann/neighbours/ ,\nhttps://github.com/enricoschumann/neighbours", "Repository": "https://enricoschumann.r-universe.dev", "Date/Publication": "2025-12-17 09:58:28 UTC", "RemoteUrl": "https://github.com/enricoschumann/neighbours", "RemoteRef": "HEAD", "RemoteSha": "c21a43e6951b12f1e7b5a539684fd464cc67b62b", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-19 08:43:03 UTC", "User": "root" }, "Author": "Enrico Schumann [aut, cre] (ORCID:\n)", "MD5sum": "2b98f29b2150e9332682fb4c2b7fc32e", "_user": "enricoschumann", "_type": "src", "_file": "neighbours_0.1-5.tar.gz", "_fileid": "f69a244a349cac580f0f96f5541dc24428699387ed2f67f743c299b31ee0dbe4", "_filesize": 374681, "_sha256": "f69a244a349cac580f0f96f5541dc24428699387ed2f67f743c299b31ee0dbe4", "_created": "2026-05-19T08:43:03.000Z", "_published": "2026-06-02T14:56:19.592Z", "_distro": 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