R Observatory: Metrics for ngboostForecast version 0.1.1

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

ngboostForecast

0.1.1

Probabilistic Time Series Forecasting

0packages depend

2.6Kdownloads / year

0.0%test coverage

13/13checks pass

Overview

About

Maintained by Resul AkayFirst published 2022-03-013 releasesCRAN page ↗GitHub ↗

Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.

Install

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

install.packages("ngboostForecast", 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("Akai01/ngboostForecast")

Development version from Akai01/ngboostForecast. 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-ngboostforecast

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

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-ngboostForecast

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-ngboostForecast

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 3.1 min · r-oldrel-windows-x86_64

Code health

Yes

Tests · ratio 0.16

0.0%

Coverage · measured lines

73%

Documentation · exports

5

Dependencies · direct

Check history

OK2026-08-05
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
NOTE2026-08-01
12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMEYes · 89 wordsVignettesNopkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo

Examples that run: 0%
Documented parameters: 50%
Return-value docs: 75%
References docs: 33%

Downloads

2.6K

CRAN downloads in the past year

Rank #17,912 · ~7/day · ~215/mo

Releases R versions

Daily download trend is not available in this view yet.

13030 days

60790 days

2.6K1 year

Compare downloads with other packages →

Also on102 r2u27 autocran

Repository

GitHub ↗ akai01/ngboostforecast

7Stars

1Forks

0Open issues

0Open PRs

2Releases

13Commits

1Contributors

probabilistic-forecastspythonrmachine-learningsklearntime-seriesngboostforecasting

13 commits · Last activity 2022-08-04

Repository practices

Upstream repositoryBeta

3 development-tooling and community-health practices detected across 3 families in the upstream repository

Checks run against github.com/akai01/ngboostforecast on 2026-08-16.

CRAN release process (1)

cran-comments.md

Docs source (1)

README.Rmd

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

7 external dependencies (excludes base and recommended)

Depends (2)

R >= 3.6reticulate

Imports (4)

dplyr forecast magrittr R6

LinkingTo (0)

none

Suggests (2)

ggplot2 testthat

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (1)

Maintainer (1)

Resul Akay

Author, Maintainer

Authors (1)

Resul Akay

Author, Maintainer

Package Timeline

3 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
0.1.1Latest
2022-08-06 · current release · diff ↗
0.1.0
2022-05-03 · diff ↗
R
R 4.2.0 released · 2022-04-22
0.0.2
2022-03-01
R
R 4.1.0 released · 2021-05-18

Package metadata

First published: 2022-03-01
Total releases: 3 / 4 yrs
License: Apache License (>= 2)
Minimum R: ≥ 3.6
Bundled data: 4.3 KB / 1 file
Download size: 110 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("ngboostForecast")

Format

Akay, R. (2022). ngboostForecast: Probabilistic Time Series Forecasting (Version 0.1.1) [Computer software]. https://doi.org/10.32614/CRAN.package.ngboostForecast

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

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 ngboostForecast version 0.1.1 [Data set]. HJJB, LLC. Data release v2026-08-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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