Package: eiIT
Title: Ecological Inference via Information Theory
Version: 0.0.1-1
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) 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
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2026-07-04 17:41:43 UTC; root
Author: Jose M. Pavía [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-0129-726X>)
Maintainer: Jose M. Pavía <jose.m.pavia@uv.es>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-06-01 08:40:07 UTC
RemoteUrl: https://github.com/cran/eiIT
RemoteRef: HEAD
RemoteSha: 8b33bb740c90eb96b262bb431332c1b34d8eabfb