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 ProximallyRegularized 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) MaximumMargin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), SoftThresholded SVD, SoftImpute matrix completion via ALS  paper by Hastie, Mazumder et al. (2014, <arXiv:1410.2596>) 4) LinearFlow matrix factorization, from 'Practical linear models for largescale oneclass collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:9781577357704) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://www.aclweb.org/anthology/D141162>) 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.
Package details 


Author  Dmitriy Selivanov [aut, cre, cph] (<https://orcid.org/0000000154131506>), Drew Schmidt [ctb] (configure script for BLAS, LAPACK detection), WeiChen Chen [ctb] (configure script and work on linking to float package) 
Maintainer  Dmitriy Selivanov <[email protected]> 
License  GPL (>= 2) 
Version  0.3.3.4 
URL  https://github.com/dselivanov/rsparse 
Package repository  View on CRAN 
Installation 
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