softImpute: Matrix Completion via Iterative Soft-Thresholded SVD

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).

Version:	1.4-1
Depends:	Matrix, methods
Suggests:	knitr, rmarkdown
Published:	2021-05-09
DOI:	10.32614/CRAN.package.softImpute
Author:	Trevor Hastie and Rahul Mazumder
Maintainer:	Trevor Hastie <hastie at stanford.edu>
License:	GPL-2
NeedsCompilation:	yes
In views:	MissingData
CRAN checks:	softImpute results

Documentation:

Reference manual:	softImpute.pdf
Vignettes:	An Introduction to softImpute

Downloads:

Package source:	softImpute_1.4-1.tar.gz
Windows binaries:	r-devel: softImpute_1.4-1.zip, r-release: softImpute_1.4-1.zip, r-oldrel: softImpute_1.4-1.zip
macOS binaries:	r-release (arm64): softImpute_1.4-1.tgz, r-oldrel (arm64): softImpute_1.4-1.tgz, r-release (x86_64): softImpute_1.4-1.tgz, r-oldrel (x86_64): softImpute_1.4-1.tgz
Old sources:	softImpute archive

Reverse dependencies:

Reverse depends:	ECLRMC
Reverse imports:	dbMC, flashier, gsbm, mashr, mimi, msImpute, NIMAA, OmicsPLS, primePCA, TrendTM, tsensembler, zinbwave

Linking:

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