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: 0.1.0
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-prerel: misspi_0.1.0.zip, r-release: misspi_0.1.0.zip, r-oldrel: misspi_0.1.0.zip
macOS binaries: r-prerel (arm64): misspi_0.1.0.tgz, r-release (arm64): misspi_0.1.0.tgz, r-oldrel (arm64): misspi_0.1.0.tgz, r-prerel (x86_64): misspi_0.1.0.tgz, r-release (x86_64): misspi_0.1.0.tgz


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