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slideimp  

Numeric Matrices K-NN and PCA Imputation
View on CRAN: Click here


Download and install slideimp package within the R console
Install from CRAN:
install.packages("slideimp")

Install from Github:
library("remotes")
install_github("cran/slideimp")

Install by package version:
library("remotes")
install_version("slideimp", "1.2.0")



Attach the package and use:
library("slideimp")
Maintained by
Hung Pham
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-07
Latest Update: 2026-01-07
Description:
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".
How to cite:
Hung Pham (2026). slideimp: Numeric Matrices K-NN and PCA Imputation. R package version 1.2.0, https://cran.r-project.org/web/packages/slideimp. Accessed 26 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:05), 0.5.4 (2026-01-07 10:20), 1.0.0 (2026-04-16 23:50), 1.1.0 (2026-05-01 13:00)
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