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  "Title": "The Topic SCORE Algorithm to Fit Topic Models",
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  "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) <arXiv:1704.07016>.",
  "License": "MIT + file LICENSE",
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  "Author": "Minzhe Wang [aut, cre], Tracy Ke [aut]",
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  "Date/Publication": "2019-06-06 10:12:14 UTC",
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