Package: filling 0.2.3
Kisung You
filling: Matrix Completion, Imputation, and Inpainting Methods
Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.
Authors:
filling_0.2.3.tar.gz
filling_0.2.3.tar.gz(r-4.5-noble)filling_0.2.3.tar.gz(r-4.4-noble)
filling_0.2.3.tgz(r-4.4-emscripten)filling_0.2.3.tgz(r-4.3-emscripten)
filling.pdf |filling.html✨
filling/json (API)
NEWS
| # Install 'filling' in R: |
| install.packages('filling', repos = c('https://cran.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 3 years agofrom:c4caae5368. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 12 2024 |
| R-4.5-linux-x86_64 | NOTE | Dec 12 2024 |
Exports:aux.rndmissingfill.HardImputefill.KNNimputefill.nuclearfill.OptSpacefill.simplefill.SoftImputefill.SVDimputefill.SVTfill.USVT
Dependencies:BHbitbit64cliCVXRECOSolveRgmplatticeMatrixnaborosqpR6rbibutilsRcppRcppArmadilloRcppEigenRdpackRmpfrROptSpaceRSpectrascs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Matrix Completion, Imputation, and Inpainting Methods | filling-package |
| Randomly assign NAs to the data matrix with probability 'x' | aux.rndmissing |
| HardImpute : Generalized Spectral Regularization | fill.HardImpute |
| Imputation using Weighted K-nearest Neighbors | fill.KNNimpute |
| Low-Rank Completion with Nuclear Norm Optimization | fill.nuclear |
| OptSpace | fill.OptSpace |
| Imputation by Simple Rules | fill.simple |
| SoftImpute : Spectral Regularization | fill.SoftImpute |
| Iterative Regression against Right Singular Vectors | fill.SVDimpute |
| Singular Value Thresholding for Nuclear Norm Optimization | fill.SVT |
| Matrix Completion by Universal Singular Value Thresholding | fill.USVT |
| lena image at size of (128 \times 128) | lena128 |
| lena image at size of (256 \times 256) | lena256 |
| lena image at size of (64 \times 64) | lena64 |
