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filling (version 0.2.4)

Matrix Completion, Imputation, and Inpainting Methods

Description

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) for an overview of the topic.

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Version

Install

install.packages('filling')

Monthly Downloads

469

Version

0.2.4

License

MIT + file LICENSE

Maintainer

Kisung You

Last Published

September 21st, 2025

Functions in filling (0.2.4)

lena128

lena image at size of \((128 \times 128)\)
lena256

lena image at size of \((256 \times 256)\)
fill.simple

Imputation by Simple Rules
fill.USVT

Matrix Completion by Universal Singular Value Thresholding
aux.rndmissing

Randomly assign NAs to the data matrix with probability x
fill.SoftImpute

SoftImpute : Spectral Regularization
fill.KNNimpute

Imputation using Weighted K-nearest Neighbors
fill.HardImpute

HardImpute : Generalized Spectral Regularization
fill.nuclear

Low-Rank Completion with Nuclear Norm Optimization
fill.SVT

Singular Value Thresholding for Nuclear Norm Optimization
fill.SVDimpute

Iterative Regression against Right Singular Vectors
fill.OptSpace

OptSpace
lena64

lena image at size of \((64 \times 64)\)