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GenForImp (version 1.0)

The Forward Imputation: A Sequential Distance-Based Approach for Imputing Missing Data

Description

Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').

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Version

Install

install.packages('GenForImp')

Monthly Downloads

14

Version

1.0

License

GPL-3

Maintainer

Alessandro Barbiero

Last Published

February 27th, 2015

Functions in GenForImp (1.0)

ForImp.PCA

Imputation of missing data by alternating Nearest Neighbour Imputation and Principal Component Analysis
GenForImp-package

The Forward Imputation: A Sequential Distance-Based Approach for Imputing Missing Data
missing.gen0

Generating random missing values on a data matrix
ForImp.Mahala

Imputation of missing data by using Nearest Neighbour Imputation with the Mahalanobis distance
missing.gen

Generating random missing values on a data matrix