{ "_id": "6a268b7424555f66ed51af25", "Package": "ACSSpack", "Type": "Package", "Title": "ACSS, Corresponding INSS, and GLP Algorithms", "Version": "1.0.0.2", "Date": "2025-10-10", "Authors@R": "c(person(\"Ziqian\", \"Yang\", role = c(\"cre\", \"aut\"), email = \"zi.yang@ufl.edu\"), \nperson(\"Kshitij\", \"Khare\", role = \"aut\"),\nperson(\"George\", \"Michailidis\", role = \"aut\"))", "Description": "Allow user to run the Adaptive Correlated Spike and Slab\n(ACSS) algorithm, corresponding INdependent Spike and Slab\n(INSS) algorithm, and Giannone, Lenza and Primiceri (GLP)\nalgorithm with adaptive burn-in. All of the three algorithms\nare used to fit high dimensional data set with either sparse\nstructure, or dense structure with smaller contributions from\nall predictors. The state-of-the-art GLP algorithm is in\nGiannone, D., Lenza, M., & Primiceri, G. E. (2021,\nISBN:978-92-899-4542-4) \"Economic predictions with big data:\nThe illusion of sparsity\". The two new algorithms, ACSS\nalgorithm and INSS algorithm, and the discussion on their\nperformance can be seen in Yang, Z., Khare, K., & Michailidis,\nG. (2024, submitted to Journal of Business & Economic\nStatistics) \"Bayesian methodology for adaptive sparsity and\nshrinkage in regression\".", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "LazyData": "true", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-08 09:25:51 UTC", "User": "root" }, "Author": "Ziqian Yang [cre, aut], Kshitij Khare [aut], George Michailidis\n[aut]", "Maintainer": "Ziqian Yang ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-10-11 04:40:02 UTC", "RemoteUrl": "https://github.com/cran/ACSSpack", "RemoteRef": "HEAD", "RemoteSha": "a98c93ed890a34db5758ccb47a66544d6aa81090", "MD5sum": "9a3c7a4058793f7a904a20c2f80b39f8", "_user": "cran", "_type": "src", "_file": "ACSSpack_1.0.0.2.tar.gz", "_fileid": "2bc30d5b1bf99e47b9f264d4b7b8687bb10431d90c5912da17fa4e353215c2d9", "_filesize": 2032578, "_sha256": "2bc30d5b1bf99e47b9f264d4b7b8687bb10431d90c5912da17fa4e353215c2d9", "_created": "2026-06-08T09:25:51.000Z", 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