R Observatory: Metrics for fEGarch version 1.0.6

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

fEGarch

1.0.6Package record

SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions

0packages depend

4.5Kdownloads / year

47.9%test coverage

13/13flavors OK

Overview

About

Maintained by Dominik SchulzCRAN page ↗

Implement and fit a variety of short-memory (SM) and long-memory (LM) models from a very broad family of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the EGARCH family is discussed in Feng et al. (2023) https://econpapers.repec.org/paper/pdnciepap/156.htm. For convenience and the purpose of comparison, a variety of other popular SM and LM GARCH-type models, like an APARCH model, a fractionally integrated APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH models, are implemented as well as dual models with simultaneous modelling of the mean, including dual long-memory models with a fractionally integrated autoregressive moving average (FARIMA) model in the mean and a long-memory model in the variance, and semiparametric volatility model extensions. Parametric models and parametric model parts are fitted through quasi-maximum-likelihood estimation. Furthermore, common forecasting and backtesting functions for value-at-risk (VaR) and expected shortfall (ES) based on the package's models are provided.

Changes in 1.0.6

GJR-GARCH, TGARCH, APARCH, FIGJR-GARCH, FITGARCH and FIAPARCH pre-sample values have been stabilized slightly.
In dual models, where for some models in the volatility part pre-sample quantities are set through empirical values, a preliminary ARMA or FARIMA estimation step has been implemented to obtain their residuals and subsequently obtain the pre-sample values based on those residuals; this stabilizes estimated conditional standard deviations at the beginning of the training period; dual models with long-memory EGF part for the variance model stay fully unaffected.
fixed a bug in garch_sim, where the simulation failed, if no setting of phi in the list for argument pars was provided manually.

From NEWS.md in the release

Install

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

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

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

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-fegarch

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-fEGarch

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-fEGarch

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: 16.1 min · r-release-linux-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK12 OK2026-04-25
NOTE12 OK, 2 NOTE2026-04-22
ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18

Show 1 earlier changeHide 1 earlier change

NOTE12 OK, 2 NOTE2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 1.0.6

READMEYes · 1,855 wordsVignettesNoNEWSYes · 100% structured · mentions 1.0.6Citation fileNoWebsitenot declared in DESCRIPTION

Help pages: 63 (1 internal)includes a package overview page
Examples that run: 98%48 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 75%
Documented parameters: 79%
Return-value docs: 100%
Help pages citing references: 25%

How these are measured

Downloads

4.5K

CRAN downloads in the past year

Rank #9,449 · ~12/day · ~373/mo

Releases R versions

Daily download trend is not available in this view yet.

40830 days

1K90 days

4.5K1 year

Compare downloads with other packages →

Also on588 r2u4 autocran

How downloads are counted

Dependencies

Declared dependencies

16 external dependencies (excludes base and recommended)

Installing fEGarch adds 57 packages (31 with compiled code, 33 MB of source tarballs). Heaviest: ggplot2 6.0 MB, mclust 2.7 MB, rugarch 2.6 MB.

Depends (2)

R >= 3.5methods

Imports (15)

Rcpp Rsolnp smoots esemifar zoostatsutilsrugarch future furrr rlang ggplot2 magrittr cli numDeriv

LinkingTo (2)

Rcpp RcppArmadillo

Suggests (1)

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 (7)

Dominik Schulz

Author, Maintainer since 1.0.0 (2025)

Paderborn University, Germany

Oliver Kojo Ayensu

Author

Paderborn University, Germany

Yuanhua Feng

Author

Paderborn University, Germany

Christian Peitz

Author

Financial Intelligence Unit (German Government)

Thomas Gries

Contributor

Paderborn University, Germany

Shujie Li

Contributor

Paderborn University, Germany

Sikandar Siddiqui

Contributor

Deloitte Audit Analytics GmbH, Frankfurt, Germany

Releases (7)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
1.0.6Latest
record
2026-02-10 · current release · diff ↗
1.0.5
record
2026-02-02 · diff ↗
1.0.4
record
2026-01-08 · diff ↗
1.0.3
record
2025-11-07 · diff ↗ ·
removed 3 exports
fEGarch_fit, fEGarch_predict, predict_internal
1.0.2
record
2025-09-11 · diff ↗
1.0.1
record
2025-06-20 · diff ↗
1.0.0
record
2025-06-11
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2025-06-11
Total releases: 7 / 1 yrs
License: GPL-3 OSI
Minimum R: ≥ 3.5
Bundled data: 58 KB / 2 files
Download size: 515 KB

Appears in task views

Empirical Finance

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("fEGarch")

Format

Schulz, D., Ayensu, O. K., Feng, Y., Gries, T., Li, S., Peitz, C., & Siddiqui, S. (2026). fEGarch: SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions (Version 1.0.6) [Computer software]. https://doi.org/10.32614/CRAN.package.fEGarch

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 fEGarch version 1.0.6 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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