{ "_id": "6ab100193d719c1f8408597c", "Package": "eiIT", "Title": "Ecological Inference via Information Theory", "Version": "0.0.2-1", "Authors@R": "person(given = \"Jose M.\",\nfamily = \"Pavía\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jose.m.pavia@uv.es\",\ncomment = c(ORCID = \"0000-0002-0129-726X\"))", "Description": "Estimates RxC transfer matrices from aggregated marginal\ndata using a two-stage (GME+IPF; Generalized Maximum Entropy\nand Iterative Proportional Fitting) information-theoretic\napproach within a two-step (global+local) estimation procedure.\nThe resulting matrices are consistent with observed row and\ncolumn marginals across collections of subtables (e.g.\nprecincts, polling stations, or districts). References: Golan,\nA., Judge, G., & Miller, D. (1996). Maximum Entropy\nEconometrics: Robust Estimation with Limited Data. Wiley.\nJudge, G., Miller, D.J., & Cho, W.K.T. (2004). \"An information\ntheoretic approach to ecological estimation and inference\". In\nG. King, O. Rosen, & M. A. Tanner (Eds.), Ecological Inference:\nNew Methodological Strategies (pp. 162–187). Cambridge\nUniversity Press. Mittelhammer, R., Judge, G., & Miller, D.\n(2000). Econometric Foundations. Cambridge University Press.\nPavia, J.M. (2023) \nAcknowledgements: The author wish to thank Conselleria de\nEconomia, Hacienda y Administracion Publica (grant\nCIACIO/2023/031) for supporting this research.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "RoxygenNote": "7.2.3", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-09-21 09:57:01 UTC", "User": "root" }, "Author": "Jose M. Pavía [aut, cre] (ORCID:\n)", "Maintainer": "Jose M. 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