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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: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | lightgbm, doParallel, doSNOW, foreach, ggplot2, glmnet, SIS, plotly |
| Suggests: | e1071, neuralnet |
| Published: | 2023-10-17 |
| Author: | Zhongli Jiang [aut, cre] |
| Maintainer: | Zhongli Jiang <jiang548 at purdue.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | misspi results |
| Reference manual: | misspi.pdf |
| Package source: | misspi_0.1.0.tar.gz |
| Windows binaries: | r-devel: misspi_0.1.0.zip, r-release: misspi_0.1.0.zip, r-oldrel: misspi_0.1.0.zip |
| macOS binaries: | r-release (arm64): misspi_0.1.0.tgz, r-oldrel (arm64): misspi_0.1.0.tgz, r-release (x86_64): misspi_0.1.0.tgz, r-oldrel (x86_64): misspi_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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