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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:f846e10752. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 08 2024 |
| R-4.5-win | OK | Nov 08 2024 |
| R-4.5-linux | OK | Nov 08 2024 |
| R-4.4-win | OK | Nov 08 2024 |
| R-4.4-mac | OK | Nov 08 2024 |
| R-4.3-win | OK | Nov 08 2024 |
| R-4.3-mac | OK | Nov 08 2024 |
Exports:artifNAartifNA.cvcomputeMAIEcomputeMSIEcomputeNRMSEcv.wNNSelwNNSelwNNSel.impute
Dependencies:
