The paper introduces a flexible R package for Nonnegative Matrix Factorization (NMF), designed to provide an open-source, easy-to-use interface for standard NMF algorithms and a framework for implementing and testing new NMF methods. The package is developed for the R/BioConductor platform and includes several published NMF algorithms and initialization methods, enabling users to combine them to create new strategies. It also provides benchmark data and visualization methods to help compare and interpret results. The package is freely available from CRAN and is intended to make NMF accessible to the broader research community, especially in bioinformatics. The package supports various NMF algorithms, including multiplicative updates, Alternate Least Squares (ALS), and non-smooth NMF. It also includes seeding methods such as random initialization and NNSVD, and provides stopping criteria for algorithm convergence. The package is flexible and extensible, allowing users to integrate new methods and test them. The paper demonstrates the package's capabilities using the Golub dataset, showing how it can be used to analyze gene expression data. The package also includes functions for visualizing results, such as heatmaps of metagene expression profiles and matrices. The paper highlights the importance of NMF in bioinformatics, particularly in gene expression analysis, and discusses its advantages over other methods. The package is available for free and is compatible with various operating systems. The authors contributed to the design and implementation of the package, and the work was supported by funding from the South-African National Bioinformatics Network.The paper introduces a flexible R package for Nonnegative Matrix Factorization (NMF), designed to provide an open-source, easy-to-use interface for standard NMF algorithms and a framework for implementing and testing new NMF methods. The package is developed for the R/BioConductor platform and includes several published NMF algorithms and initialization methods, enabling users to combine them to create new strategies. It also provides benchmark data and visualization methods to help compare and interpret results. The package is freely available from CRAN and is intended to make NMF accessible to the broader research community, especially in bioinformatics. The package supports various NMF algorithms, including multiplicative updates, Alternate Least Squares (ALS), and non-smooth NMF. It also includes seeding methods such as random initialization and NNSVD, and provides stopping criteria for algorithm convergence. The package is flexible and extensible, allowing users to integrate new methods and test them. The paper demonstrates the package's capabilities using the Golub dataset, showing how it can be used to analyze gene expression data. The package also includes functions for visualizing results, such as heatmaps of metagene expression profiles and matrices. The paper highlights the importance of NMF in bioinformatics, particularly in gene expression analysis, and discusses its advantages over other methods. The package is available for free and is compatible with various operating systems. The authors contributed to the design and implementation of the package, and the work was supported by funding from the South-African National Bioinformatics Network.