Numeric Matrices K-NN and PCA Imputation R package slideimp version 1.2.0 Fast k-nearest neighbors (K-NN) and principal component analysis (PCA) imputation algorithms for missing values in epigenetic data or other high-dimensional numeric matrices. For PCA, a locally optimal block preconditioned conjugate gradient (LOBPCG) eigensolver with warm starts of both the eigenblock and search direction is also supported. Two complementary imputation strategies are available. Group-wise imputation (e.g., by chromosome) is recommended for Illumina DNA methylation microarrays (e.g., 450K, EPIC) and other matrices with groupable columns. A sliding window approach for K-NN or PCA imputation is recommended only for whole-genome methylation data such as whole-genome bisulfite sequencing (WGBS) or Enzymatic Methyl-seq (EM-seq). The package also supports hyperparameter tuning via repeated cross-validation. 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 reimplementation 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". Software Depends: R (>= 4.3.0) Imports: bigmemory, checkmate, cli, collapse, mirai, Rcpp, stats, utils LinkingTo: Rcpp, RcppArmadillo, RcppThread Suggests: knitr, missMDA, RhpcBLASctl, rmarkdown, testthat (>= 3.0.0), withr Hung Pham <amser.hoanghung@gmail.com> Comprehensive R Archive Network (CRAN) Hung Pham [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-8271-9355>), Posit Software, PBC [cph] (Copyright holder of code adapted from the 'carrier' package, MIT licensed) GPL (>= 2) 2026-06-16 application/tgz https://CRAN.R-project.org/package=slideimp doi:10.32614/CRAN.package.slideimp en-US