Package: eiIT
Title: Ecological Inference via Information Theory
Version: 0.0.2-1
Authors@R: 
    person(given = "Jose M.",
           family = "Pavía",
           role = c("aut", "cre"),
           email = "jose.m.pavia@uv.es",
           comment = c(ORCID = "0000-0002-0129-726X"))
Description: 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) <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.
License: GPL (>= 2)
Encoding: UTF-8
Imports: stats, utils, nloptr
Suggests: ggplot2, scales, testthat (>= 3.0.0)
RoxygenNote: 7.2.3
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-08-22 15:36:14 UTC; mariaamparo
Author: Jose M. Pavía [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-0129-726X>)
Maintainer: Jose M. Pavía <jose.m.pavia@uv.es>
Repository: CRAN
Date/Publication: 2026-08-22 16:00:12 UTC
Built: R 4.6.0; ; 2026-08-22 18:51:50 UTC; unix
