R Observatory: Metrics for betaARMA version 1.2.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

betaARMA

1.2.0Package record

Beta Autoregressive Moving Average Models

0packages depend

3.3Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Everton da CostaCRAN page ↗GitHub ↗Issues ↗

Fits Beta Autoregressive Moving Average (BARMA) models for time series data distributed in the standard unit interval (0, 1). The estimation is performed via the conditional maximum likelihood method using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton algorithm. A ridge penalization scheme is available to improve numerical stability of the estimation, as proposed by Cribari-Neto, Costa and Fonseca (2025) doi:10.1214/25-BJPS645. The package includes tools for model fitting, diagnostic checking, and forecasting, along with two hydro-environmental datasets from Brazil. Based on the work of Rocha and Cribari-Neto (2009) doi:10.1007/s11749-008-0112-z and the associated erratum Rocha and Cribari-Neto (2017) doi:10.1007/s11749-017-0528-4. The original code was developed by Fabio M. Bayer.

Changes in 1.2.0

New features

Add S3 method plot.barma() to generate diagnostic plots for fitted barma objects. It displays a four-panel grid by default: observed vs. fitted values, residuals over time, residual ACF, and a residual histogram with a density overlay.
Add penalty argument to barma(), enabling ridge-penalized conditional maximum likelihood estimation as proposed by Cribari-Neto, Costa and Fonseca (2025, BJPS). When penalty = TRUE, the penalized log-likelihood is optimized and the penalized FIM is returned.
Add internal helper utils_internal.R with .barma_ridge_lambda() to centralize the ridge penalty scalar lambda_n = 1/(n-a)^0.9, shared across loglik_barma(), score_vector_barma(), and fim_barma().

Show all changes in 1.2.0Hide the rest of the changes in 1.2.0

Add utils_optimizer.R with internal helpers make_par_indices() and run_optimizer(), centralizing parameter indexing and optimization logic previously embedded in barma().

Improvements

Forward penalty argument through run_optimizer() to loglik_barma() and score_vector_barma(), ensuring the penalized likelihood is used consistently during optimization when requested.
Forward penalty argument to fim_barma() in barma(), so the returned Fisher Information Matrix reflects penalization when penalty = TRUE.

Documentation

Add two new vignettes: one detailing the numerical stability and ridge penalization approach (Cribari-Neto, Costa, and Fonseca, 2025), and another demonstrating applied hydro-environmental time series modeling (Costa, Cribari-Neto, and Scher, 2024).
Add Cribari-Neto, Costa and Fonseca (2025, BJPS, DOI: 10.1214/25-BJPS645) to @references in barma(), reflecting the new penalty argument.
Update @param optimization in barma() to use \describe instead of \itemize for correct rendering of named list fields in HTML help pages.

From NEWS.md in the release

Install

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

install.packages("betaARMA", 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("Everton-da-Costa/betaARMA")

Development version from Everton-da-Costa/betaARMA. 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-betaarma

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-betaARMA

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-betaARMA

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: 2.6 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-09-08
ERROR12 OK, 1 ERROR2026-09-07
OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-05-11

Show 2 earlier changesHide 2 earlier changes

WARNING12 OK, 1 WARNING2026-05-10
OK7 OK2026-03-30

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 1.2.0

READMEYes · 1,101 wordsVignettesYes · 1 · dynamicNEWSYes · 100% structured · mentions 1.2.0Citation fileYes · cites a publicationWebsitenot declared in DESCRIPTION

Help pages: 18 (1 internal)
Examples that run: 60%6 run in a plain check · 4 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 86%
Documented parameters: 96%
Return-value docs: 100%
Help pages citing references: 33% with a bibliography file

Vignettes (1)

Application: Modeling Relative Humidity in Brasília

How these are measured

Downloads

3.3K

CRAN downloads in the past year

Rank #7,576 · ~9/day · ~274/mo

Releases R versions

Daily download trend is not available in this view yet.

38930 days

1.2K90 days

3.3K1 year

Compare downloads with other packages →

Also on96 r2u18 autocran

How downloads are counted

Repository

everton-da-costa/betaarma ↗Account: Everton-da-Costa

0Stars

0Forks

0Open issues

0Open PRs

0Releases

barmacranforecastingrstatisticstime-series

Last activity 2026-06-18

Responsiveness

not measured

How these are measured

PracticesBeta

7 development-tooling and community-health practices detected across 5 families in the upstream repository

Checks run against github.com/everton-da-costa/betaarma on 2026-10-25.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

Docs source (1)

README.Rmd

Reproducibility and dev environment (1)

data-raw/

CRAN release process (2)

cran-comments.mdCRAN-SUBMISSION

Show all practicesHide 1 practice

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies

15 external dependencies (excludes base and recommended)

Installing betaARMA adds 29 packages (16 with compiled code, 22 MB of source tarballs). Heaviest: ggplot2 6.0 MB, colorspace 2.0 MB, Rcpp 1.8 MB.

Depends (1)

R >= 3.5

Imports (4)

forecast ggplot2 rlang gridExtra

LinkingTo (0)

none

Suggests (11)

knitr zoo xtable here moments rmarkdown tseries lbfgs dplyr scales testthat

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)

Everton da Costa

Author, Maintainer since 1.0.1 (2026)

Francisco Cribari-Neto

Contributor, Thesis advisor

Theoretical foundations

Vinicius Scher

Contributor

Releases (3)

Pick two releases to compare their code metrics.

1.2.0Latest
record
2026-05-23 · current release · diff ↗
R
R 4.6.0 released · 2026-04-24
1.1.0
record
2026-04-15 · diff ↗ ·
added 1 export
score_vector_barma
1.0.1
record
2026-03-29
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2026-03-29
Total releases: 3 / 1 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 3.5
Bundled data: 4.6 KB / 2 files
Download size: 393 KB

Appears in task views

Time Series Analysis

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("betaARMA")

The package provides a citation file that builds its entry when R runs it. Run citation() to see it.

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 betaARMA version 1.2.0 [Data set]. HJJB, LLC. Data release v2026-10-26. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-26, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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