This package introduces new non-parametric tools for the imputation of missing values in high-dimensional data. It includes weighted nearest neighbor imputation methods that use distances for selected covariates. The careful selection of distances that carry information about the missing values yields an imputation tool. It does not require pre-specified k, unlike other kNN methods. It can be used to impute missing values in high-dimensional data when n<p.
|Depends:||R (>= 2.10)|
|License:||GPL (>= 2)|
The main function of the package is
wNNSel for implementing the nonparameteric procedure of nearest neighbors imputaiton.
wNNSel for more details.
*Author's Last name changed to Faisal from Ramzan in 2016.
Shahla Faisal <firstname.lastname@example.org>
Tutz, G. and Ramzan,S*. (2015). Improved methods for the imputation of missing data by nearest neighbor methods. Computational Statistics and Data Analysis, Vol. 90, pp. 84-99.
Faisal, S.* and Tutz, G. (2017). Missing value imputation for gene expression data by tailored nearest neighbors. Statistical Application in Genetics and Molecular Biology. Vol. 16(2), pp. 95-106.
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