Package: wNNSel 0.1

wNNSel: Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.

Authors:Shahla Faisal

wNNSel_0.1.tar.gz
wNNSel_0.1.zip(r-4.5)wNNSel_0.1.zip(r-4.4)wNNSel_0.1.zip(r-4.3)
wNNSel_0.1.tgz(r-4.4-any)wNNSel_0.1.tgz(r-4.3-any)
wNNSel_0.1.tar.gz(r-4.5-noble)wNNSel_0.1.tar.gz(r-4.4-noble)
wNNSel_0.1.tgz(r-4.4-emscripten)wNNSel_0.1.tgz(r-4.3-emscripten)
wNNSel.pdf |wNNSel.html
wNNSel/json (API)

# Install 'wNNSel' in R:
install.packages('wNNSel', repos = c('https://shahlafaisal.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 8 scripts 459 downloads 8 exports 0 dependencies

Last updated 7 years agofrom:f846e10752. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 09 2024
R-4.5-winOKOct 09 2024
R-4.5-linuxOKOct 09 2024
R-4.4-winOKOct 09 2024
R-4.4-macOKOct 09 2024
R-4.3-winOKOct 09 2024
R-4.3-macOKOct 09 2024

Exports:artifNAartifNA.cvcomputeMAIEcomputeMSIEcomputeNRMSEcv.wNNSelwNNSelwNNSel.impute

Dependencies: