{ "_id": "6ab7682299f7aa8c6aa30351", "Package": "RMVL", "Title": "Mappable Vector Library for Handling Large Datasets", "Version": "1.1.0.4", "Authors@R": "person(given = \"Vladimir\",\nfamily = \"Dergachev\",\nrole = c(\"aut\", \"cre\"),\nemail = \"support@altumrete.com\",\ncomment = c(ORCID=\"https://orcid.org/0000-0003-4708-6625\"))", "Description": "Mappable vector library provides convenient way to access\nlarge datasets. Use all of your data at once, with few limits.\nMemory mapped data can be shared between multiple R processes.\nAccess speed depends on storage medium, so solid state drive is\nrecommended, preferably with PCI Express (or M.2 nvme)\ninterface or a fast network file system. The data is memory\nmapped into R and then accessed using usual R list and array\nsubscription operators. Convenience functions are provided for\nmerging, grouping and indexing large vectors and data.frames.\nThe layout of underlying MVL files is optimized for large\ndatasets. The vectors are stored to guarantee alignment for\nvector intrinsics after memory map. The package is built on top\nof libMVL, which can be used as a standalone C library. libMVL\nhas simple C API making it easy to interchange datasets with\noutside programs. Large MVL datasets are distributed via\nAcademic Torrents\n.", "URL": "https://academictorrents.com/collection/mvl-datasets,\nhttps://github.com/volodya31415/RMVL,\nhttps://github.com/volodya31415/libMVL", "License": "LGPL-2.1", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "Repository": "https://volodya31415.r-universe.dev", "Date/Publication": "2026-08-27 01:05:30 UTC", "RemoteUrl": "https://github.com/volodya31415/rmvl", "RemoteRef": "HEAD", "RemoteSha": "5aa6159f060dfbacda5457eecb7f65d87c75a44d", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-09-26 06:30:59 UTC", "User": "root" }, "Author": "Vladimir Dergachev [aut, cre] (ORCID:\n)", "Maintainer": "Vladimir Dergachev ", "_user": "volodya31415", "_type": "src", "_file": "RMVL_1.1.0.4.tar.gz", "_fileid": "https://r2.ropensci.org/0a3e849f1200650c475a5297eb00a229637ef37bfb3fcaefc89566b3540528ad", "_filesize": 134941, "_sha256": "0a3e849f1200650c475a5297eb00a229637ef37bfb3fcaefc89566b3540528ad", "_expires": 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