Missing Value Imputation in Parallel

A framework that boosts the imputation of 'missForest' by Stekhoven, D.J. and Bühlmann, P. (2012) 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.

misspi

Missing Value Imputation in Parallel

Install From R CRAN

install.packages("misspi")

Tutorial

Please find a more detailed tutorial here

Quick Start

data(toxicity, package = "misspi")
set.seed(0)
toxicity.miss <- missar(toxicity, 0.4, 0.2)
toxicity.impute <- misspi(toxicity.miss)
toxicity.impute