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  "Description": "A framework that boosts the imputation of 'missForest' by\nStekhoven, D.J. and Bühlmann, P. (2012)\n<doi:10.1093/bioinformatics/btr597> by harnessing parallel\nprocessing and through the fast Gradient Boosted Decision Trees\n(GBDT) implementation 'LightGBM' by Ke, Guolin et al.(2017)\n<https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.\n'misspi' has the following main advantages: 1. Allows\nembrassingly parallel imputation on large scale data. 2.\nAccepts a variety of machine learning models as methods with\nfriendly user portal. 3. Supports multiple initializations\nmethods. 4. Supports early stopping that prohibits unnecessary\niterations.",
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