{ "_id": "6a27d3e324555f66ed545913", "Package": "midasml", "Type": "Package", "Title": "Estimation and Prediction Methods for High-Dimensional Mixed\nFrequency Time Series Data", "Version": "0.1.11", "Authors@R": "c(\nperson(\"Jonas\", \"Striaukas\", role = c(\"cre\",\"aut\"), email = \"jonas.striaukas@gmail.com\"),\nperson(\"Andrii\", \"Babii\", role = c(\"aut\"),\nemail = \"andrii@email.unc.edu\"),\nperson(\"Jad\", \"Beyhum\", role = c(\"aut\"),\nemail = \"jad.beyhum@kuleuven.be\"),\nperson(c(\"Eric\", \"Ghysels\"), role = c(\"aut\"),\nemail = \"eghysels@unc.edu\"),\nperson(\"Alex\", \"Kostrov\", role = c(\"ctb\"),\ncomment = \"Contributions to analytical gradients for non-linear low-dimensional MIDAS estimation code\", email = \"alexander.kostrov@unisg.ch\"))", "Maintainer": "Jonas Striaukas ", "Description": "The 'midasml' package implements estimation and prediction\nmethods for high-dimensional mixed-frequency (MIDAS)\ntime-series and panel data regression models. The regularized\nMIDAS models are estimated using orthogonal (e.g. Legendre)\npolynomials and sparse-group LASSO (sg-LASSO) estimator. For\nmore information on the 'midasml' approach see Babii, Ghysels,\nand Striaukas (2021, JBES forthcoming)\n. The package is equipped\nwith the fast implementation of the sg-LASSO estimator by means\nof proximal block coordinate descent. High-dimensional mixed\nfrequency time-series data can also be easily manipulated with\nfunctions provided in the package.", "BugReports": "https://github.com/jstriaukas/midasml/issues", "License": "GPL (>= 2)", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-09 08:47:48 UTC", "User": "root" }, "Author": "Jonas Striaukas [cre, aut], Andrii Babii [aut], Jad Beyhum\n[aut], Eric Ghysels [aut], Alex Kostrov [ctb] (Contributions to\nanalytical gradients for non-linear low-dimensional MIDAS\nestimation code)", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-10-09 06:46:37 UTC", "RemoteUrl": "https://github.com/cran/midasml", "RemoteRef": "HEAD", "RemoteSha": "2bb972e64718024889dc72ff9712e7f451aa6679", "MD5sum": "0f0456d718565fe6a734d037d3cf1354", "_user": "cran", "_type": "src", "_file": "midasml_0.1.11.tar.gz", "_fileid": "01950f2e27a84da7d48121870779c0d44acf5bd2e316a6a62e01299aaa72a763", "_filesize": 954248, 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