{ "_id": "6a499bfd4ae8ee07ee1337a7", "Package": "midasml", "Type": "Package", "Title": "Estimation and Prediction Methods for High-Dimensional Mixed\nFrequency Time Series Data", "Version": "0.1.10", "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(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", "Repository": "https://jstriaukas.r-universe.dev", "Date/Publication": "2022-09-29 12:08:44 UTC", "RemoteUrl": "https://github.com/jstriaukas/midasml", "RemoteRef": "HEAD", "RemoteSha": "a2e547ce1e883c5340813ea9cb4d2d448720f571", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-04 23:45:11 UTC", "User": "root" }, "Author": "Jonas Striaukas [cre, aut],\nAndrii Babii [aut],\nEric Ghysels [aut],\nAlex Kostrov [ctb] (Contributions to analytical gradients for\nnon-linear low-dimensional MIDAS estimation code)", "_user": "jstriaukas", "_type": "src", "_file": "midasml_0.1.10.tar.gz", "_fileid": "https://r2.ropensci.org/918b00c3df513c324e94c6a321fca1da205f8a81775d59d7f4a4d0a75bf980ae", "_filesize": 950540, "_sha256": 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