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  "Title": "Volume-Regularized Structured Matrix Factorization",
  "Version": "1.0.2",
  "Authors@R": "c(person(\"Ruslan\", \"Soldatov\", email = \"ruslansoldatov@gmail.com\", role = c(\"aut\")), person(\"Peter\", \"Kharchenko\", email = \"peter_kharchenko@hms.harvard.edu\", role = \"aut\"), person(\"Viktor\", \"Petukhov\", email = \"viktor.s.petukhov@ya.ru\", role = c(\"aut\")), person(\"Evan\", \"Biederstedt\", email=\"evan.biederstedt@gmail.com\", role=c(\"cre\", \"aut\")))",
  "Description": "Implements a set of routines to perform structured matrix\nfactorization with minimum volume constraints. The NMF\nprocedure decomposes a matrix X into a product C * D. Given\nconditions such that the matrix C is non-negative and has\nsufficiently spread columns, then volume minimization of a\nmatrix D delivers a correct and unique, up to a scale and\npermutation, solution (C, D). This package provides both an\nimplementation of volume-regularized NMF and \"anchor-free\" NMF,\nwhereby the standard NMF problem is reformulated in the\ncovariance domain. This algorithm was applied in Vladimir B.\nSeplyarskiy Ruslan A. Soldatov, et al. \"Population sequencing\ndata reveal a compendium of mutational processes in the human\ngerm line\". Science, 12 Aug 2021.\n<doi:10.1126/science.aba7408>. This package interacts with data\navailable through the 'simulatedNMF' package, which is\navailable in a 'drat' repository. To access this data package,\nsee the instructions at\n<https://github.com/kharchenkolab/vrnmf>. The size of the\n'simulatedNMF' package is approximately 8 MB.",
  "License": "GPL-3",
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  "URL": "https://github.com/kharchenkolab/vrnmf",
  "BugReports": "https://github.com/kharchenkolab/vrnmf/issues",
  "Author": "Ruslan Soldatov [aut], Peter Kharchenko [aut], Viktor Petukhov\n[aut], Evan Biederstedt [cre, aut]",
  "Maintainer": "Evan Biederstedt <evan.biederstedt@gmail.com>",
  "Repository": "https://kharchenkolab.r-universe.dev",
  "Date/Publication": "2022-04-14 14:35:05 UTC",
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      "page": "AnchorFree",
      "title": "Non-negative tri-factorization of co-occurence matrix using minimum volume approach.",
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      "title": "Simulate matrices to explores 'vrnmf'",
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