<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>jorgevazcabral.r-universe.dev</title><link>https://jorgevazcabral.r-universe.dev</link><description>Recent package updates in jorgevazcabral</description><generator>R-universe</generator><image><url>https://github.com/jorgevazcabral.png</url><title>R packages by jorgevazcabral</title><link>https://jorgevazcabral.r-universe.dev</link></image><lastBuildDate>Sun, 07 Jun 2026 09:39:46 GMT</lastBuildDate><item><title>[jorgevazcabral] GCEstim 1.1.0</title><author>jorgecabral@ua.pt (Cabral Jorge)</author><description>Estimation and inference using the Generalized Maximum
Entropy (GME) and Generalized Cross Entropy (GCE) framework, a
flexible method for solving ill-posed inverse problems and
parameter estimation under uncertainty (Golan, Judge, and
Miller (1996, ISBN:978-0471145925) &quot;Maximum Entropy
Econometrics: Robust Estimation with Limited Data&quot;). The
package includes routines for generalized cross entropy
estimation of linear models including the implementation of a
GME-GCE two steps approach. Diagnostic tools, and options to
incorporate prior information through support and prior
distributions are available (Macedo, Cabral, Afreixo, Macedo
and Angelelli (2025) &lt;doi:10.1007/978-3-031-97589-9_21&gt;). In
particular, support spaces can be defined by the user or be
internally computed based on the ridge trace or on the
distribution of standardized regression coefficients. Different
optimization methods for the objective function can be used. An
adaptation of the normalized entropy aggregation (Macedo and
Costa (2019) &lt;doi:10.1007/978-3-030-26036-1_2&gt; &quot;Normalized
entropy aggregation for inhomogeneous large-scale data&quot;) and a
two-stage maximum entropy approach for time series regression
(Macedo (2022) &lt;doi:10.1080/03610918.2022.2057540&gt;) are also
available. Suitable for applications in econometrics, health,
signal processing, and other fields requiring robust estimation
under data constraints.</description><link>https://github.com/r-universe/jorgevazcabral/actions/runs/27089902271</link><pubDate>Sun, 07 Jun 2026 09:39:46 GMT</pubDate><r:package>GCEstim</r:package><r:version>1.1.0</r:version><r:status>success</r:status><r:repository>https://jorgevazcabral.r-universe.dev</r:repository><r:upstream>https://github.com/jorgevazcabral/gcestim</r:upstream><r:article><r:source>V5_Choosing_Supports.Rmd</r:source><r:filename>V5_Choosing_Supports.html</r:filename><r:title>Choosing the supports spaces</r:title><r:created>2025-10-07 11:43:26</r:created><r:modified>2026-06-06 15:15:21</r:modified></r:article><r:article><r:source>V7_Further_Considerations.Rmd</r:source><r:filename>V7_Further_Considerations.html</r:filename><r:title>Further considerations</r:title><r:created>2025-10-07 11:43:26</r:created><r:modified>2026-06-05 16:38:43</r:modified></r:article><r:article><r:source>V3_GCE_framework.Rmd</r:source><r:filename>V3_GCE_framework.html</r:filename><r:title>Generalized Cross Entropy framework</r:title><r:created>2025-10-07 11:43:26</r:created><r:modified>2026-06-05 10:32:24</r:modified></r:article><r:article><r:source>V2_GME_framework.Rmd</r:source><r:filename>V2_GME_framework.html</r:filename><r:title>Generalized Maximum Entropy framework</r:title><r:created>2025-10-07 11:43:26</r:created><r:modified>2026-06-05 10:09:57</r:modified></r:article><r:article><r:source>V4_Optimization.Rmd</r:source><r:filename>V4_Optimization.html</r:filename><r:title>Optimization methods</r:title><r:created>2025-10-07 11:43:26</r:created><r:modified>2026-06-05 10:46:15</r:modified></r:article><r:article><r:source>V1_Quick_start.Rmd</r:source><r:filename>V1_Quick_start.html</r:filename><r:title>Quick start</r:title><r:created>2025-10-07 11:43:26</r:created><r:modified>2026-06-06 18:12:11</r:modified></r:article><r:article><r:source>V6_Two_steps_ME_estimation.Rmd</r:source><r:filename>V6_Two_steps_ME_estimation.html</r:filename><r:title>Two-stage ME estimation</r:title><r:created>2025-10-07 11:43:26</r:created><r:modified>2026-06-05 16:50:14</r:modified></r:article></item></channel></rss>