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: 2026-09-21 09:57:01 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: 2026-08-22 16:45:32 UTC
RemoteUrl: https://github.com/cran/eiIT
RemoteRef: HEAD
RemoteSha: 12cfb319d8d9953f84d01551daba397a6227eb30
