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eiIT  

Ecological Inference via Information Theory
View on CRAN: Click here


Download and install eiIT package within the R console
Install from CRAN:
install.packages("eiIT")

Install from Github:
library("remotes")
install_github("cran/eiIT")

Install by package version:
library("remotes")
install_version("eiIT", "0.0.1-1")



Attach the package and use:
library("eiIT")
Maintained by
Jose M. Pavía
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-01
Latest Update: 2026-06-01
Description:
Estimates RxC transfer matrices from aggregated marginal data using a two-stage (GME+IPF) information-theoretic approach within a two-step (global+local) estimation procedure. The resulting matrices are consistent with observed row and column marginals across collections of subtables (e.g. precincts, polling stations, or districts). References: Golan, A., Judge, G., & Miller, D. (1996). Maximum Entropy Econometrics: Robust Estimation with Limited Data. Wiley. Judge, G., Miller, D.J., & Cho, W.K.T. (2004). An information theoretic approach to ecological estimation and inference. In G. King, O. Rosen, & M. A. Tanner (Eds.), Ecological Inference: New Methodological Strategies (pp. 162–187). Cambridge University Press. Mittelhammer, R., Judge, G., & Miller, D. (2000). Econometric Foundations. Cambridge University Press. Pavia, J.M. (2023) <doi:10.1007/s43545-023-00658-y> Acknowledgements: The author wish to thank Conselleria de Economia, Hacienda y Administracion Publica (grant CIACIO/2023/031) for supporting this research.
How to cite:
Jose M. Pavía (2026). eiIT: Ecological Inference via Information Theory. R package version 0.0.1-1, https://cran.r-project.org/web/packages/eiIT. Accessed 21 Aug. 2026.
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