{ "_id": "6a1e90801d7bb097a0a6a0d6", "Package": "TopicScore", "Title": "The Topic SCORE Algorithm to Fit Topic Models", "Version": "0.0.1", "Authors@R": "c(person(\"Minzhe\", \"Wang\", role = c(\"aut\", \"cre\"),\nemail = \"minzhew@uchicago.edu\"),\nperson(\"Tracy\", \"Ke\", role = \"aut\"))", "Description": "Provides implementation of the \"Topic SCORE\" algorithm\nthat is proposed by Tracy Ke and Minzhe Wang. The singular\nvalue decomposition step is optimized through the usage of\nsvds() function in 'RSpectra' package, on a 'dgRMatrix' sparse\nmatrix. Also provides a column-wise error measure in the\nword-topic matrix A, and an algorithm for recovering the\ntopic-document matrix W given A and D based on quadratic\nprogramming. The details about the techniques are explained in\nthe paper \"A new SVD approach to optimal topic estimation\" by\nTracy Ke and Minzhe Wang (2017) .", "License": "MIT + file LICENSE", "LazyData": "true", "RoxygenNote": "6.1.1", "Author": "Minzhe Wang [aut, cre], Tracy Ke [aut]", "Maintainer": "Minzhe Wang ", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 08:09:34 UTC", "User": "root" }, "Repository": "https://minzhew.r-universe.dev", "Date/Publication": "2019-06-06 10:12:14 UTC", "RemoteUrl": "https://github.com/cran/TopicScore", "RemoteRef": "HEAD", "RemoteSha": "17fc161e3f4a9aed1131ca3afb36403925a3fd3f", "MD5sum": "485c6013d592983e301fdb867df00046", "_user": "minzhew", "_type": "src", "_file": "TopicScore_0.0.1.tar.gz", "_fileid": "a540f888c7923f6b9e50c1e8f6f783933c0b7e071fca07ad27e447ff0386c394", "_filesize": 516705, "_sha256": "a540f888c7923f6b9e50c1e8f6f783933c0b7e071fca07ad27e447ff0386c394", "_created": "2026-06-02T08:09:34.000Z", "_published": 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