slideimp: Numeric Matrices K-NN and PCA Imputation
Fast k-nearest neighbors (K-NN) and principal component
analysis (PCA) imputation algorithms for missing values in
high-dimensional numeric matrices, i.e., epigenetic data. For
extremely high-dimensional data with ordered features, a sliding
window approach for K-NN or PCA imputation is provided. Additional
features include group-wise imputation (e.g., by chromosome),
hyperparameter tuning with repeated cross-validation, multi-core
parallelization, and optional subset imputation. The K-NN algorithm is
described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M.,
Brown, P. and Botstein, D. (1999) "Imputing Missing Data for Gene
Expression Arrays". The PCA imputation is an optimized version of the
imputePCA() function from the 'missMDA' package described in: Josse,
J. and Husson, F. (2016) <doi:10.18637/jss.v070.i01> "missMDA: A
Package for Handling Missing Values in Multivariate Data Analysis".
| Version: |
0.5.4 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
bigmemory, checkmate, collapse, mirai, purrr, Rcpp, stats, tibble |
| LinkingTo: |
mlpack, Rcpp, RcppArmadillo, RcppEnsmallen |
| Suggests: |
carrier, FactoMineR, knitr, missMDA, rlang, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-01-07 |
| DOI: |
10.32614/CRAN.package.slideimp |
| Author: |
Hung Pham  [aut,
cre, cph] |
| Maintainer: |
Hung Pham <amser.hoanghung at gmail.com> |
| BugReports: |
https://github.com/hhp94/slideimp/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/hhp94/slideimp |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
slideimp results |
Documentation:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.