Ecological Inference via Information Theory R package eiIT version 0.0.1-1 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. Software Imports: stats, utils, nloptr Suggests: ggplot2, scales Jose M. Pavía <jose.m.pavia@uv.es> Comprehensive R Archive Network (CRAN) Jose M. Pavía [aut, cre] (ORCID: <https://orcid.org/0000-0002-0129-726X>) GPL (>= 2) 2026-06-01 application/tgz https://CRAN.R-project.org/package=eiIT doi:10.32614/CRAN.package.eiIT