{ "_id": "6acf6c3de2d863f662951e98", "Package": "nnmf", "Type": "Package", "Title": "Nonnegative Matrix Factorization", "Version": "1.6", "Date": "2026-07-27", "Authors@R": "c( person(\"Michail\", \"Tsagris\", role = c(\"aut\", \"cre\"), email = \"mtsagris@uoc.gr\"), \nperson(\"Nikolaos\", \"Kontemeniotis\", role = \"aut\", email = \"kontemeniotisn@gmail.com\" ),\nperson(\"Christos\", \"Adam\", role = \"aut\", email = \"pada4m4@gmail.com\", comment = c(ORCID = \"0009-0003-3244-7034\") ))", "Maintainer": "Michail Tsagris ", "Description": "Nonnegative matrix factorization (NMF) is a technique to\nfactorize a matrix with nonnegative values into the product of\ntwo matrices. Covariates are also allowed. Parallel computing\nis an option to enhance the speed and high-dimensional and\nlarge scale (and/or sparse) data are allowed. Relevant papers\ninclude: Sevinc V., Kontemeniotis N., Perdikis T. and Tsagris\nM. (2026). Non-negative matrix factorization using the R\npackage nnmf , Wang Y. X. and\nZhang Y. J. (2012). Nonnegative matrix factorization: A\ncomprehensive review. IEEE Transactions on Knowledge and Data\nEngineering, 25(6): 1336-1353 and\nKim H. and Park H. (2008). Nonnegative matrix factorization\nbased on alternating nonnegativity constrained least squares\nand active set method. 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