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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | misspi_0.1.1.tar.gz |
630.5 KiB |
0.1.1 |
rolling linux/noble R-4.5 | misspi_0.1.1.tar.gz |
630.3 KiB |
0.1.1 |
rolling source/ R- | misspi_0.1.1.tar.gz |
410.0 KiB |
0.1.1 |
latest linux/jammy R-4.5 | misspi_0.1.1.tar.gz |
630.5 KiB |
0.1.1 |
latest linux/noble R-4.5 | misspi_0.1.1.tar.gz |
630.3 KiB |
0.1.1 |
latest source/ R- | misspi_0.1.1.tar.gz |
410.0 KiB |
0.1.1 |
2026-04-26 source/ R- | misspi_0.1.1.tar.gz |
410.0 KiB |
0.1.1 |
2026-04-23 source/ R- | misspi_0.1.1.tar.gz |
410.0 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | misspi_0.1.1.zip |
633.3 KiB |
0.1.0 |
2025-04-20 source/ R- | misspi_0.1.0.tar.gz |
405.9 KiB |