A Program for Missing Data
Amelia: A Program for Missing Data
AMELIA: Multiple Imputation of Incomplete Multivariate Data
Combine multiple runs of Amelia
Interactive GUI for Amelia
Combine Multiple Amelia Output Lists
Compare observed versus imputed densities
Overdispersed starting values diagnostic for multiple imputation
Combine results from analyses on imputed data sets
Combine Multiple Results From Multiply Imputed Datasets
Missingness Map
Prepare Multiple Overimputation Settings
Overimputation diagnostic plot
Summary plots for Amelia objects
Summary of an Amelia object
Summary of an mi object
Transform imputed datasets from Amelia objects
Plot observed and imputed time-series for a single cross-section
Execute commands within each imputed data set
Write Amelia imputations to file
A tool that "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R.