mice - Multivariate Imputation by Chained Equations
Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Last updated 23 days ago
chained-equationsfcsimputationmicemissing-datamissing-valuesmultiple-imputationmultivariate-datacpp
16.27 score 447 stars 142 dependents 8.4k scripts 60k downloads
ggmice - Visualizations for 'mice' with 'ggplot2'
Enhance a 'mice' imputation workflow with visualizations for incomplete and/or imputed data. The plotting functions produce 'ggplot' objects which may be easily manipulated or extended. Use 'ggmice' to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data.
Last updated 5 months ago
ggplot2micevisualization
7.68 score 31 stars 156 scripts 567 downloads