filling-package: Matrix Completion, Imputation, and Inpainting Methods
In filling: 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. Currently, we have following methods implemented,

Name of Function	Method
`fill.HardImpute`	Generalized Spectral Regularization
`fill.KNNimpute`	Weighted K-nearest Neighbors
`fill.nuclear`	Nuclear Norm Optimization
`fill.OptSpace`	OptSpace
`fill.simple`	Simple Rules of Mean, Median, and Random
`fill.SoftImpute`	Spectral Regularization
`fill.SVDimpute`	Iterative Regression against Right Singular Vectors
`fill.SVT`	Singular Value Thresholding for Nuclear Norm Optimization
`fill.USVT`	Universal Singular Value Thresholding