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
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libssl-dev
Repository: https://catstats.r-universe.dev
Date/Publication: 2026-01-25 05:32:01 UTC
RemoteUrl: https://github.com/catstats/misspi
RemoteRef: HEAD
RemoteSha: 2cd288b71abb34a838a05612ecc17440e3483af2
NeedsCompilation: no
Packaged: 2026-10-10 17:28:05 UTC; root
Author: Zhongli Jiang [aut, cre]
Maintainer: Zhongli Jiang <happycatstat@gmail.com>
Depends: R (>= 3.5.0)