c(
person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="selivanov.dmitriy@gmail.com",
comment = c(ORCID = "0000-0001-5413-1506")),
person("Drew", "Schmidt", role="ctb", comment="configure script for BLAS, LAPACK detection"),
person("Wei-Chen", "Chen", role="ctb", comment="considerable contributions with correct
linking to the float package")
)
Dmitriy Selivanov [aut, cre, cph]
(<https://orcid.org/0000-0001-5413-1506>),
Drew Schmidt [ctb] (configure script for BLAS, LAPACK detection),
Wei-Chen Chen [ctb] (considerable contributions with correct linking to
the float package)

c(
person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="ds@rexy.ai",
comment = c(ORCID = "0000-0001-5413-1506")),
person("Drew", "Schmidt", role="ctb", comment="configure script for BLAS, LAPACK detection"),
person("Wei-Chen", "Chen", role="ctb", comment="configure script and work on linking to float package")
)

c(
person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="ds@rexy.ai",
comment = c(ORCID = "0000-0001-5413-1506")),
person("David", "Cortes", role="ctb"),
person("Drew", "Schmidt", role="ctb", comment="configure script for BLAS, LAPACK detection"),
person("Wei-Chen", "Chen", role="ctb", comment="configure script and work on linking to float package")
)
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://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.
Dmitriy Selivanov [aut, cre, cph]
(<https://orcid.org/0000-0001-5413-1506>),
David Cortes [ctb],
Drew Schmidt [ctb] (configure script for BLAS, LAPACK detection),
Wei-Chen Chen [ctb] (configure script and work on linking to float
package)