R Observatory: Metrics for GCEstim version 1.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

GCEstim

1.1.0Package record

Regression Coefficients Estimation Using the Generalized Cross Entropy

0packages depend

3.1Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Cabral JorgeCRAN page ↗GitHub ↗Issues ↗

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) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). 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) doi:10.1007/978-3-031-97589-9_21). 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) doi:10.1007/978-3-030-26036-1_2 "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) doi:10.1080/03610918.2022.2057540) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

Changes in 1.1.0

Major update of the package;
Added TASW-GCE and TARW-GCE estimators for adaptive support-space estimation.
Corrected the implementation of the 1se selection rule.
Updated vignettes and examples with new data sets.
TASW-GCE and TARW-GCE parameters improved.
Objective function adapted for faster computation.

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("GCEstim", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("jorgevazcabral/GCEstim")

Development version from jorgevazcabral/GCEstim. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-gcestim

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-GCEstim

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-GCEstim

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 8.0 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK13 OK2026-06-07

Show 4 earlier changesHide 4 earlier changes

ERROR11 OK, 2 ERROR2026-05-22
OK13 OK2026-05-20
ERROR12 OK, 1 ERROR2026-05-19
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 1.1.0

READMEYes · 187 wordsVignettesYes · 7 · dynamicNEWSYes · 100% structured · mentions 1.1.0Citation fileNoWebsitenot declared in DESCRIPTION

Help pages: 66
Examples that run: 67%34 run in a plain check · 17 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 76%
Documented parameters: 95%
Return-value docs: 100%
Help pages citing references: 12%

Vignettes (7)

How these are measured

Downloads

3.1K

CRAN downloads in the past year

Rank #9,481 · ~8/day · ~256/mo

Releases R versions

Daily download trend is not available in this view yet.

53230 days

1K90 days

3.1K1 year

Compare downloads with other packages →

Also on69 r2u2 autocran

How downloads are counted

Repository

jorgevazcabral/gcestim ↗Account: jorgevazcabral

0Stars

0Forks

0Open issues

0Open PRs

2Releases

94Commits

1Commit identities

94 commits · Last activity 2026-06-07

Responsiveness

not measured

Releases over time

2025-10-07 · 12026-09-22 · 2

How these are measured

PracticesBeta

2 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/jorgevazcabral/gcestim on 2026-10-18.

Docs source (1)

pkgdown

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies

34 external dependencies (excludes base and recommended)

Installing GCEstim adds 175 packages (84 with compiled code, 152 MB of source tarballs). Heaviest: BH 14 MB, stringi 11 MB, ggplot2 6.0 MB.

Depends (2)

R >= 3.5.0zoo

LinkingTo (0)

none

Suggests (4)

knitr rmarkdown kableExtra NlcOptim

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (3)

Cabral Jorge

Author, Maintainer since 0.1.0 (2025)

Macedo Pedro

Thesis advisor

Afreixo Vera

Thesis advisor

Releases (2)

Pick two releases to compare their code metrics.

1.1.0Latest
record
2026-06-07 · current release · diff ↗ ·
added 3 exports
cv.dynlmgce, cv.tsbootgce, dynlmgce
R
R 4.6.0 released · 2026-04-24
0.1.0
record
2025-07-16
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2025-07-16
Total releases: 2 / 1 yrs
License: GPL-3 OSI
Minimum R: ≥ 3.5.0
Bundled data: 418 KB / 8 files
Download size: 7.0 MB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("GCEstim")

Format

Jorge, C., Pedro, M., & Vera, A. (2026). GCEstim: Regression Coefficients Estimation Using the Generalized Cross Entropy (Version 1.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.GCEstim

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for GCEstim version 1.1.0 [Data set]. HJJB, LLC. Data release v2026-10-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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