eiIT
0.0.1-1Ecological Inference via Information Theory
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-027 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
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- Documented parameters
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- Return-value docs
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- References docs
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Code & Tests
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1 release. R releases are shown for context.
- 0.0.1-1Latest2026-06-01 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-01
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 2) OSI
- Download size
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