Nonnegative Matrix Factorization R package nnmf version 1.6 Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Sevinc V., Kontemeniotis N., Perdikis T. and Tsagris M. (2026). Non-negative matrix factorization using the R package nnmf <doi:10.48550/arXiv.2607.20084>, Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6): 1336-1353 <doi:10.1109/TKDE.2012.51> and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2): 713-730 <doi:10.1137/07069239X>. Software Depends: R (>= 4.0) Imports: Rcpp, ClusterR, Compositional, graphics, Matrix, osqp, parallel, quadprog, rangen, Rfast, Rglpk, sparcl, stats LinkingTo: Rcpp, RcppEigen Suggests: Rfast2 Michail Tsagris <mtsagris@uoc.gr> Comprehensive R Archive Network (CRAN) Michail Tsagris [aut, cre], Nikolaos Kontemeniotis [aut], Christos Adam [aut] (ORCID: <https://orcid.org/0009-0003-3244-7034>) GPL (>= 2) 2026-07-27 application/tgz https://CRAN.R-project.org/package=nnmf doi:10.32614/CRAN.package.nnmf