Amelia II: A Program for Missing Data

By James Honaker, Gary King, M. Blackwell. 2011

2,764 citations recorded by the source corpus.

Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, thereby including a great deal of potentially valuable and extensive information. It also includes features to accurately impute cross-sectional datasets, individual time series, or sets of time series for different cross-sections. A full set of graphical diagnostics are also available. The program is easy to use, and the simplicity of the algorithm makes it far more robust; both a simple command line and extensive graphical user interface are included.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bf1ee22af84f7fab614ce9a258d04165eec88f23

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Citations in the corpus

imputeTS: Time Series Missing Value Imputation in R (2017, neutral): “The most popular and mature (among others) are AMELIA (Honaker et al., 2011), mice (van Buuren and Groothuis-Oudshoorn, 2011), VIM (Kowarik and Templ, 2016) and missMDA (Josse and Husson, 2016).”
Gender, work orientations and job satisfaction (2015, methodology): “The multiple imputation was performed using the R package ‘Amelia II’ developed by Honaker et al. (2011).”
GAIN: Missing Data Imputation using Generative Adversarial Nets (2018, methodology): “We use R to implement MICE (R package MICE (Buuren & Groothuis-Oudshoorn, 2011)), MissForest (R package MissForest (Stekhoven, 2013)), Matrix Completion (R package softImpute (Hastie & Mazumder, 2015)), and EM (R package Amelia (Honaker et al., 2011)).”
Maximum Likelihood Multiple Imputation: Faster Imputations and Consistent Standard Errors Without Posterior Draws (2012, neutral): “Faster algorithms are available to get PD estimates [22], and estimation can be further accelerated by running the algorithm in parallel [17, 29].”
On the joys of missing data. (2014, background): “The stem was worded to be specific to the PE class setting, for example: ‘‘In this PE class, I usually find time flies.’’”
Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data (2016, methodology): “Methods of imputation included Amelia II (Honaker et al. 2011), Multiple Imputation Using Chained Equations (Buuren and Groothuis-Oudshoorn 2011), and Random Forest (Breiman 2001a).”