Package: softImpute
Type: Package
Title: Matrix Completion via Iterative Soft-Thresholded SVD
Version: 1.4-1
Date: 2021-5-08
Author: Trevor Hastie <hastie@stanford.edu> and Rahul Mazumder <rahul.mazumder@gmail.com>
Maintainer: Trevor Hastie <hastie@stanford.edu>
Description: Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an 'EM' flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components).
License: GPL-2
Depends: Matrix, methods
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2021-05-09 00:14:29 UTC; hastie
Repository: CRAN
Date/Publication: 2021-05-09 05:10:03 UTC
Built: R 4.0.3; x86_64-w64-mingw32; 2021-05-11 06:04:12 UTC; windows
Archs: x64
