misspi: Missing Value Imputation in Parallel
A framework that boosts the imputation of 'missForest' by Stekhoven, D.J. and Bühlmann, P. (2012) <doi:10.1093/bioinformatics/btr597> by harnessing parallel processing and through the fast Gradient Boosted Decision Trees (GBDT) implementation 'LightGBM' by Ke, Guolin et al.(2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. 'misspi' has the following main advantages: 1. Allows embrassingly parallel imputation on large scale data. 2. Accepts a variety of machine learning models as methods with friendly user portal. 3. Supports multiple initializations methods. 4. Supports early stopping that prohibits unnecessary iterations.
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
lightgbm, doParallel, doSNOW, foreach, ggplot2, glmnet, SIS, plotly |
| Suggests: |
e1071, neuralnet |
| Published: |
2026-01-25 |
| DOI: |
10.32614/CRAN.package.misspi |
| Author: |
Zhongli Jiang [aut, cre] |
| Maintainer: |
Zhongli Jiang <happycatstat at gmail.com> |
| BugReports: |
https://github.com/catstats/misspi/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/catstats/misspi |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
misspi results [issues need fixing before 2026-01-30] |
Documentation:
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