Matrix Completion via Iterative Soft-Thresholded SVD
Overview
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).
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 19 (1 internal)includes a package overview page
- Examples that run
- 100%10 run in a plain check
- Exported functions with examples
- 50%
- Documented parameters
- 100%
- Return-value docs
- 50%
- Help pages citing references
- 26%
Downloads
Dependencies
Installing softImpute adds no packages beyond those that come with R.
Code & Tests
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- RR 4.6.0 released · 2026-04-24
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- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
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- RR 4.1.0 released · 2021-05-18
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- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
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- RR 3.2.0 released · 2015-04-16
- 1.4
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- RR 3.1.0 released · 2014-04-10
- 1.02013-04-03
- RR 3.0.0 released · 2013-04-03
- RR 2.15.0 released · 2012-03-30
Package metadata
- First published
- 2013-04-03
- Total releases
- 5 / 13 yrs
- License
- GPL-2 OSI
- Download size
- 252 KB
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