Amelia II: A Program for Missing Data
Open accessAbstract
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.
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The Amelia II R package was selected due to the statistical validity of its approach in handling time-series, cross-sectional data.
“The Amelia II R package was selected due to the statistical validity of its approach in handling time-series, cross-sectional data (Honaker & King, 2010;Honaker et al., 2011).”
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- Authors
- Honaker, J., King, G., & Blackwell, M.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2011
- Volume
- 45
- Issue
- 7
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W1822348759
- Cited by
- 2,552
- References
- 2
- Citations by year
- 2026: 78 · 2025: 119 · 2024: 136 · 2023: 144 · 2022: 155 · 2021: 238 · 2020: 221 · 2019: 212 · 2018: 223 · 2017: 174 · 2016: 151 · 2015: 159 · 2014: 150 · 2013: 109 · 2012: 66
- FWCI
- 103.98
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Statistical Software (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Keywords
- multiple imputation, missing data, prior distributions, time series imputation, cross-sectional data, graphical diagnostics, R package
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