{ "_id": "6a2549e64b233be19839ba9f", "Package": "GCEstim", "Type": "Package", "Title": "Regression Coefficients Estimation Using the Generalized Cross\nEntropy", "Version": "1.1.0", "Date": "2026-06-07", "Authors@R": "c(\nperson(\"Cabral\", \"Jorge\", email = \"jorgecabral@ua.pt\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-5721-4550\")),\nperson(\"Macedo\", \"Pedro\", role = c(\"ths\")),\nperson(\"Afreixo\", \"Vera\", role = c(\"ths\"))\n)", "Description": "Estimation and inference using the Generalized Maximum\nEntropy (GME) and Generalized Cross Entropy (GCE) framework, a\nflexible method for solving ill-posed inverse problems and\nparameter estimation under uncertainty (Golan, Judge, and\nMiller (1996, ISBN:978-0471145925) \"Maximum Entropy\nEconometrics: Robust Estimation with Limited Data\"). The\npackage includes routines for generalized cross entropy\nestimation of linear models including the implementation of a\nGME-GCE two steps approach. Diagnostic tools, and options to\nincorporate prior information through support and prior\ndistributions are available (Macedo, Cabral, Afreixo, Macedo\nand Angelelli (2025) ). In\nparticular, support spaces can be defined by the user or be\ninternally computed based on the ridge trace or on the\ndistribution of standardized regression coefficients. Different\noptimization methods for the objective function can be used. An\nadaptation of the normalized entropy aggregation (Macedo and\nCosta (2019) \"Normalized\nentropy aggregation for inhomogeneous large-scale data\") and a\ntwo-stage maximum entropy approach for time series regression\n(Macedo (2022) ) are also\navailable. Suitable for applications in econometrics, health,\nsignal processing, and other fields requiring robust estimation\nunder data constraints.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "URL": "https://github.com/jorgevazcabral/GCEstim", "BugReports": "https://github.com/jorgevazcabral/GCEstim/issues", "Config/spelling": "wordlist: inst/WORDLIST", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev\nzlib1g-dev", "Repository": "https://jorgevazcabral.r-universe.dev", "Date/Publication": "2026-06-07 09:39:46 UTC", "RemoteUrl": "https://github.com/jorgevazcabral/gcestim", "RemoteRef": "HEAD", "RemoteSha": "c30469ecf3854ed36a25d73bedff633803d67164", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-07 10:33:00 UTC", "User": "root" }, "Author": "Cabral Jorge [aut, cre] (ORCID:\n),\nMacedo Pedro [ths],\nAfreixo Vera [ths]", "Maintainer": "Cabral Jorge ", "MD5sum": 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"title": "Extract 'cv.tsbootgce' Model Coefficients", "topics": [ "coef.cv.tsbootgce" ] }, { "page": "coef.dataThesis", "title": "Coefficients used in simulated data set generated with fngendata", "topics": [ "coef.dataThesis" ] }, { "page": "coef.lmgce", "title": "Extract 'lmgce' Model Coefficients", "topics": [ "coef.lmgce" ] }, { "page": "coef.neagging", "title": "Extract 'neagging' Coefficients", "topics": [ "coef.neagging" ] }, { "page": "coef.ridgetrace", "title": "Extract 'ridgetrace' Model Coefficients", "topics": [ "coef.ridgetrace" ] }, { "page": "coef.tsbootgce", "title": "Extract 'tsbootgce' Model Coefficients", "topics": [ "coef.tsbootgce" ] }, { "page": "coefficients.cv.lmgce", "title": "Extract 'cv.lmgce' Coefficients", "topics": [ "coefficients.cv.lmgce" ] }, { "page": "coefficients.cv.tsbootgce", "title": "Extract 'cv.tsbootgce' Model Coefficients", "topics": [ "coefficients.cv.tsbootgce" ] }, { "page": "coefficients.lmgce", "title": "Extract 'lmgce' Model Coefficients", "topics": [ "coefficients.lmgce" ] }, { "page": "coefficients.neagging", "title": "Extract 'neagging' Coefficients", "topics": [ "coefficients.neagging" ] }, { "page": "coefficients.ridgetrace", "title": "Extract 'ridgetrace' Model Coefficients", "topics": [ "coefficients.ridgetrace" ] }, { "page": "coefficients.tsbootgce", "title": "Extract 'tsbootgce' Model Coefficients", "topics": [ "coefficients.tsbootgce" ] }, { "page": "confint.cv.tsbootgce", "title": "Confidence Intervals for 'cv.tsbootgce' Model Parameters and Normalized Entropy", "topics": [ "confint.cv.tsbootgce" ] }, { "page": "confint.lmgce", "title": "Confidence Intervals for 'lmgce' Model Parameters and Normalized Entropy", "topics": [ "confint.lmgce" ] }, { "page": "confint.tsbootgce", "title": "Confidence Intervals for 'tsbootgce' Model Parameters and Normalized Entropy", "topics": [ "confint.tsbootgce" ] }, { "page": "cv.dynlmgce", "title": "Cross-validation for 'dynlmgce'", "topics": [ "cv.dynlmgce" ] }, { "page": "cv.lmgce", "title": "Cross-validation for 'lmgce'", "topics": [ "cv.lmgce" ] }, { "page": "cv.tsbootgce", "title": "Time series bootstrap Cross entropy estimation", "topics": [ "cv.tsbootgce" ] }, { "page": "dataExample", "title": "Simulated data set generated with fngendata", "topics": [ "dataExample" ] }, { "page": "dataGCE", "title": "Simulated data set generated with fngendata", "topics": [ "dataGCE" ] }, { "page": "dataGCE.test", "title": "Simulated data set generated with fngendata", "topics": [ "dataGCE.test" ] }, { "page": "dataincRidGME", "title": "Simulated data set generated with fngendata", "topics": [ "dataincRidGME" ] }, { "page": "dataincRidGME.test", "title": "Simulated data set generated with fngendata", "topics": [ "dataincRidGME.test" ] }, { "page": "dataThesis", "title": "Simulated data set generated with fngendata", "topics": [ "dataThesis" ] }, { "page": "df.residual.lmgce", "title": "Residual Degrees-of-Freedom", "topics": [ "df.residual.lmgce" ] }, { "page": "dynlmgce", "title": "Dynamic Linear Models and Time Series Regression using Cross entropy estimation", "topics": [ "dynlmgce" ] }, { "page": "ER.test", "title": "Entropy Ratio test", "topics": [ "ER.test" ] }, { "page": "fitted.lmgce", "title": "Calculate 'lmgce' Fitted Values", "topics": [ "fitted.lmgce" ] }, { "page": "fitted.values.lmgce", "title": "Calculate 'lmgce' Fitted Values", "topics": [ "fitted.values.lmgce" ] }, { "page": "fngendata", "title": "Data generating function", "topics": [ "fngendata" ] }, { "page": "formula.lmgce", "title": "Extract Model Formula from 'lmgce' object", "topics": [ "formula.lmgce" ] }, { "page": "lmgce", "title": "Generalized Cross entropy estimation", "topics": [ "lmgce" ] }, { "page": "lmgceAddin", "title": "An add-in to easily generate the code for a 'lmgce' or 'cv.lmgce' analysis", "topics": [ "lmgceAddin" ] }, { "page": "lmgceAPP", "title": "'lmgce' Shiny application", "topics": [ "lmgceAPP" ] }, { "page": "model.matrix.lmgce", 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