eiIT: Ecological Inference via Information Theory

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.

Version:	0.0.1-1
Imports:	stats, utils, nloptr
Suggests:	ggplot2, scales
Published:	2026-06-01
DOI:	10.32614/CRAN.package.eiIT
Author:	Jose M. Pavía [aut, cre]
Maintainer:	Jose M. Pavía <jose.m.pavia at uv.es>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	eiIT results

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