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".
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | slideimp_1.0.0.tar.gz |
354.2 KiB |
1.0.0 |
rolling linux/noble R-4.5 | slideimp_1.0.0.tar.gz |
361.2 KiB |
1.0.0 |
rolling source/ R- | slideimp_1.0.0.tar.gz |
104.5 KiB |
1.0.0 |
latest linux/jammy R-4.5 | slideimp_1.0.0.tar.gz |
354.2 KiB |
1.0.0 |
latest linux/noble R-4.5 | slideimp_1.0.0.tar.gz |
361.2 KiB |
1.0.0 |
latest source/ R- | slideimp_1.0.0.tar.gz |
104.5 KiB |
1.0.0 |
2026-04-26 source/ R- | slideimp_1.0.0.tar.gz |
104.5 KiB |
1.0.0 |
2026-04-23 source/ R- | slideimp_1.0.0.tar.gz |
104.5 KiB |
0.5.4 |
2026-04-09 windows/windows R-4.5 | slideimp_0.5.4.zip |
999.3 KiB |