{ "_id": "6a1021f1acfb0bcc41c8c8f3", "Package": "sparsestep", "Version": "1.0.1", "Date": "2021-01-10", "Title": "SparseStep Regression", "Authors@R": "c(person(\"Gertjan\", \"van den Burg\", role=c(\"aut\", \"cre\"), \nemail=\"gertjanvandenburg@gmail.com\"),\nperson(\"Patrick\", \"Groenen\", email=\"groenen@ese.eur.nl\", role=\"ctb\"),\nperson(\"Andreas\", \"Alfons\", email=\"alfons@ese.eur.nl\", role=\"ctb\"))", "Description": "Implements the SparseStep model for solving regression\nproblems with a sparsity constraint on the parameters. The\nSparseStep regression model was proposed in Van den Burg,\nGroenen, and Alfons (2017) . In the model, a\nregularization term is added to the regression problem which\napproximates the counting norm of the parameters. By\niteratively improving the approximation a sparse solution to\nthe regression problem can be obtained. In this package both\nthe standard SparseStep algorithm is implemented as well as a\npath algorithm which uses golden section search to determine\nsolutions with different values for the regularization\nparameter.", "License": "GPL (>= 2)", "Classification/MSC": "62J05, 62J07", "URL": "https://github.com/GjjvdBurg/SparseStep,\nhttps://arxiv.org/abs/1701.06967", "BugReports": "https://github.com/GjjvdBurg/SparseStep", "RoxygenNote": "7.1.0", "Repository": "https://gjjvdburg.r-universe.dev", "Date/Publication": "2021-01-12 16:31:08 UTC", "RemoteUrl": "https://github.com/gjjvdburg/sparsestep", "RemoteRef": "HEAD", "RemoteSha": "4c49fabae8e6c4392dd6afa0c0be8731b16f1cee", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-14 05:12:50 UTC", "User": "root" }, "Author": "Gertjan van den Burg [aut, cre],\nPatrick Groenen [ctb],\nAndreas Alfons [ctb]", "Maintainer": "Gertjan van den Burg ", "MD5sum": "df0cb658670c8274918e38dc32e8c20c", "_user": "gjjvdburg", "_type": "src", "_file": 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