rsparse

Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, doi:10.1145/2487575.2488200) 2) Factorization Machines via SGD, as per Rendle (2010, doi:10.1109/ICDM.2010.127) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, doi:10.1109/ICDM.2008.22) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, doi:10.1145/1102351.1102441) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, doi:10.48550/arXiv.1410.2596) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, https://aclanthology.org/D14-1162/) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems.

Last 30 days

This package has been downloaded 9,037 times in the last 30 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 264 times.

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Last 365 days

This package has been downloaded 61,442 times in the last 365 days. An impressive feat! Enough downloads to make even seasoned academics take note. The day with the most downloads was Feb 18, 2025 with 570 downloads.

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