{"package":"r-rsparse","ecosystem":"conda","latest_version":"0.5.3","description":"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 <sparse, dense> 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, <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://www.aclweb.org/anthology/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.","license":"GPL-2.0-or-later","license_risk":"unknown","commercial_use_notes":"verify manually — license not parseable / not declared.","homepage":"https://github.com/dselivanov/rsparse","repository":"","downloads_weekly":310,"health":{"score":49,"risk":"high","breakdown":{"maintenance":10,"popularity":3,"security":25,"maturity":9,"community":2},"deprecated":false,"max_score":100},"vulnerabilities":{"count":0,"critical":0,"high":0,"medium":0,"low":0,"details":[]},"versions":{"latest":"0.5.3","total_count":6,"recent":["0.3.3.4","0.4.0","0.5.0","0.5.1","0.5.2","0.5.3"]},"metadata":{"deprecated":false,"deprecated_message":null,"maintainers_count":1,"first_published":"2021-05-25 21:26:34.921000+00:00","last_published":"2025-09-14 13:31:32.507000+00:00","dependencies_count":0,"dependencies":[]},"github_stats":null,"bundle":null,"typescript":null,"known_issues":{"bugs_count":0,"bugs_severity":{},"status_breakdown":{},"link":null,"scope":"none"},"historical_compromise":null,"recommendation":{"action":"safe_to_use","issues":[],"use_version":"0.5.3","version_hint":null,"summary":"r-rsparse@0.5.3 is safe to use (health: 49/100)"},"version_scoped":null,"requested_version":null,"_cache":"hit","_response_ms":0,"_powered_by":"depscope.dev — free package intelligence for AI agents","typosquat":{"is_suspected":false},"maintainer_trust":{"available":false},"malicious":{"is_malicious":false},"scorecard":{"available":false},"quality":{"available":false},"version_history_summary":{"total_versions":6,"first_release_age_days":1799,"last_release_days_ago":226,"avg_days_between_releases":360,"release_velocity":"moderate"}}