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softImpute  

Matrix Completion via Iterative Soft-Thresholded SVD
View on CRAN: Click here


Download and install softImpute package within the R console
Install from CRAN:
install.packages("softImpute")

Install from Github:
library("remotes")
install_github("cran/softImpute")

Install by package version:
library("remotes")
install_version("softImpute", "1.4-1")



Attach the package and use:
library("softImpute")
Maintained by
Trevor Hastie
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-04-03
Latest Update: 2021-05-09
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).
How to cite:
Trevor Hastie (2013). softImpute: Matrix Completion via Iterative Soft-Thresholded SVD. R package version 1.4-1, https://cran.r-project.org/web/packages/softImpute
Previous versions and publish date:
1.0 (2013-04-03 07:17), 1.4 (2015-04-08 00:42)
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