Package: misspi 0.1.0

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.

Authors:Zhongli Jiang [aut, cre]

misspi_0.1.0.tar.gz
misspi_0.1.0.zip(r-4.5)misspi_0.1.0.zip(r-4.4)misspi_0.1.0.zip(r-4.3)
misspi_0.1.0.tgz(r-4.4-any)misspi_0.1.0.tgz(r-4.3-any)
misspi_0.1.0.tar.gz(r-4.5-noble)misspi_0.1.0.tar.gz(r-4.4-noble)
misspi_0.1.0.tgz(r-4.4-emscripten)misspi_0.1.0.tgz(r-4.3-emscripten)
misspi.pdf |misspi.html✨
misspi/json (API)

Peer review:

Bug tracker:https://github.com/catstats/misspi/issues

On CRAN:

3 exports 1 stars 1.02 score 83 dependencies 179 downloads

Last updated 27 days agofrom:6cd240dfda. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:evaliqmissarmisspi

Dependencies:askpassbase64encbslibcachemclicodetoolscolorspacecpp11crosstalkcurldata.tabledigestdoParalleldoSNOWdplyrevaluatefansifarverfastmapfontawesomeforeachfsgenericsggplot2glmnetgluegtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelightgbmmagrittrMASSMatrixmemoisemgcvmimemunsellncvregnlmeopensslpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesshapeSISsnowstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml