Package: misspi
Type: Package
Title: Missing Value Imputation in Parallel
Version: 0.1.1
Authors@R: person("Zhongli", "Jiang", role = c("aut", "cre"), email = "happycatstat@gmail.com")
Description: 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.
License: GPL-2
URL: https://github.com/catstats/misspi
BugReports: https://github.com/catstats/misspi/issues
Encoding: UTF-8
LazyData: true
Imports: lightgbm, doParallel, doSNOW, foreach, ggplot2, glmnet, SIS,
        plotly
Suggests: e1071, neuralnet
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2026-01-25 09:03:48 UTC; jiangzhongli
Author: Zhongli Jiang [aut, cre]
Maintainer: Zhongli Jiang <happycatstat@gmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2026-01-25 21:50:08 UTC
Built: R 4.6.0; ; 2026-04-25 23:46:10 UTC; unix
