DESCRIPTION

Package: rsparse
Type: Package
Title: Statistical Learning on Sparse Matrices
Version: 0.3.2
Authors@R: c(
    person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="selivanov.dmitriy@gmail.com"),
    person("Drew", "Schmidt", role="ctb", comment="configure script for BLAS, LAPACK detection"), 
    person("Han", "Zhang", role="ctb", comment="configure script for OpenMP detection"), 
    person("Wei-Chen", "Chen", role="ctb", comment="configure script and work on linking to float package")
    )
Maintainer: Dmitriy Selivanov <selivanov.dmitriy@gmail.com>
Description: Implements several algorithms for
    supervised learning on sparse data and many
    matrix factorizations of sparse matrices
    (with a focus on applications for recommender systems). 
    All algorithms work on sparse matrices. Also they extensively 
    use BLAS and LAPACK  and parallelized with OpenMP.
    Implementations are reasonably fast and nicely work with large 
    datasets (millions of rows and millions of columns).
    List of algorithms for supervised learning:
    1) Elastic net regression via Follow The Proximally-Regularized leader algorithm
    2) Second order Factorization Machines via stochastic gradient descent with 
       adaptive learning rates. Allows to learn model parameters out-of-core. 
       Fast - asynchronous parallel, SIMD accelerated.
    List of algorithms for matrix factorization:
    1) Weighted Regularazied Matrix Factorization with Alternating Least Squares (ALS) 
      for implicit feedback (inculding approximate Conjugate Gradient solver).
      Optional non-negativity (NNMF, non-negative matrix factorization).
    2) Regularazied Matrix Factorization with ALS for explicit feedback
      Optional non-negativity (NNMF, non-negative matrix factorization).
    3) Fast Trunceate SVD and Soft-SVD via ALS
    4) Soft-Impute via fast ALS and solution in SVD form
    5) LinearFlow method which learns item-item similarity matrix from the data
    6) GloVe - GlobalVectors embeddings
    Clustering:
    1) kmeans from Armadillo library which provides smart (similar to kmeans++) 
      cluster initializations.
    Misc utils/methods:
    1) multithreaded `%*%` and `tcrossprod()` for `<dgRMatrix, matrix>`
    2) multithreaded `%*%` and `crossprod()` for `<matrix, dgCMatrix>`
License: GPL (>= 2) | file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
Depends: R (>= 3.1.0), methods
Imports: Matrix, Rcpp, mlapi (>= 0.1.0), data.table (>= 1.10.0),
        futile.logger, RhpcBLASctl, float (>= 0.2-0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat
RoxygenNote: 6.0.1
NeedsCompilation: yes