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cff-version: 1.2.0
message: 'To cite package "eiIT" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'eiIT: Ecological Inference via Information Theory'
version: 0.0.1-1
abstract: '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) <https://doi.org/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.'
authors:
- family-names: Pavía
  given-names: Jose M.
  email: jose.m.pavia@uv.es
  orcid: https://orcid.org/0000-0002-0129-726X
repository: https://cran.r-universe.dev
commit: 8b33bb740c90eb96b262bb431332c1b34d8eabfb
date-released: '2026-06-01'
contact:
- family-names: Pavía
  given-names: Jose M.
  email: jose.m.pavia@uv.es
  orcid: https://orcid.org/0000-0002-0129-726X