Ecological Inference via Information Theory

Estimates RxC transfer matrices from aggregated marginal data using a two-stage (GME+IPF; Generalized Maximum Entropy and Iterative Proportional Fitting) 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) Acknowledgements: The author wish to thank Conselleria de Economia, Hacienda y Administracion Publica (grant CIACIO/2023/031) for supporting this research.


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install.packages("eiIT")

0.0.2-1 by Jose M. Pavía, 2 months ago


Browse source code at https://github.com/cran/eiIT


Authors: Jose M. Pavía [aut, cre] (ORCID:


Documentation:   PDF Manual  


GPL (>= 2) license


Imports stats, utils, nloptr

Suggests ggplot2, scales, testthat


See at CRAN