Package: softImpute 1.4-1
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).
Authors:
softImpute_1.4-1.tar.gz
softImpute_1.4-1.zip(r-4.5)softImpute_1.4-1.zip(r-4.4)softImpute_1.4-1.zip(r-4.3)
softImpute_1.4-1.tgz(r-4.4-x86_64)softImpute_1.4-1.tgz(r-4.4-arm64)softImpute_1.4-1.tgz(r-4.3-x86_64)softImpute_1.4-1.tgz(r-4.3-arm64)
softImpute_1.4-1.tar.gz(r-4.5-noble)softImpute_1.4-1.tar.gz(r-4.4-noble)
softImpute_1.4-1.tgz(r-4.4-emscripten)softImpute_1.4-1.tgz(r-4.3-emscripten)
softImpute.pdf |softImpute.html✨
softImpute/json (API)
| # Install 'softImpute' in R: |
| install.packages('softImpute', repos = c('https://trevorhastie.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:b497771bee. Checks:OK: 7 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 06 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 06 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 06 2024 |
| R-4.4-win-x86_64 | OK | Nov 06 2024 |
| R-4.4-mac-x86_64 | OK | Nov 06 2024 |
| R-4.4-mac-aarch64 | OK | Nov 06 2024 |
| R-4.3-win-x86_64 | OK | Nov 06 2024 |
| R-4.3-mac-x86_64 | OK | Nov 06 2024 |
| R-4.3-mac-aarch64 | OK | Nov 06 2024 |
Exports:as.matrixbiScaleclean.warm.startcoercecolMeanscolSumscompletedeBiasimputeIncompletelambda0normrowMeansrowSumssimpute.alssimpute.svdsoftImputesoftImpute.x.IncompletesoftImpute.x.matrixsplrSsimpute.alsSsimpute.svdSsvd.alssvd.als
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| standardize a matrix to have optionally row means zero and variances one, and/or column means zero and variances one. | biScale |
| make predictions from an svd object | complete complete,Incomplete-method complete,matrix-method impute |
| Recompute the '$d' component of a '"softImpute"' object through regression. | deBias |
| create a matrix of class 'Incomplete' | coerce,matrix-method Incomplete |
| Class '"Incomplete"' | as.matrix,Incomplete-method coerce,matrix,Incomplete-method coerce,sparseMatrix,Incomplete-method Incomplete-class |
| compute the smallest value for 'lambda' such that 'softImpute(x,lambda)' returns the zero solution. | lambda0 lambda0,Incomplete-method lambda0,sparseMatrix-method lambda0,SparseplusLowRank-method |
| impute missing values for a matrix via nuclear-norm regularization. | softImpute |
| Class '"SparseplusLowRank"' | %*%,ANY,SparseplusLowRank-method %*%,Matrix,SparseplusLowRank-method %*%,SparseplusLowRank,ANY-method %*%,SparseplusLowRank,Matrix-method as.matrix,SparseplusLowRank-method colMeans,SparseplusLowRank-method colSums,SparseplusLowRank-method dim,SparseplusLowRank-method norm,SparseplusLowRank,character-method rowMeans,SparseplusLowRank-method rowSums,SparseplusLowRank-method SparseplusLowRank-class |
| create a 'SparseplusLowRank' object | splr |
| compute a low rank soft-thresholded svd by alternating orthogonal ridge regression | svd.als svd.als,sparseMatrix-method svd.als,SparseplusLowRank-method |
