R Observatory: Metrics for skedastic version 2.0.3

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

skedastic

2.0.3Package record

Handling Heteroskedasticity in the Linear Regression Model

0packages dependsuggested by 1

25.3Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Thomas FarrarCRAN page ↗GitHub ↗Issues ↗

Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) doi:10.1007/978-0-387-21606-5. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) doi:10.1080/00031305.2015.1089789. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) doi:10.1016/j.jeconom.2008.08.003. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) https://projecteuclid.org/euclid.bsmsp/1200512155, Ramsey's (1969) BAMSET Test doi:10.1111/j.2517-6161.1969.tb00796.x, the tests of Bickel (1978) doi:10.1214/aos/1176344124, Breusch and Pagan (1979) doi:10.2307/1911963 with and without the modification proposed by Koenker (1981) doi:10.1016/0304-4076(81)90062-2, Carapeto and Holt (2003) doi:10.1080/0266476022000018475, Cook and Weisberg (1983) doi:10.1093/biomet/70.1.1 (including their graphical methods), Diblasi and Bowman (1997) doi:10.1016/S0167-7152(96)00115-0, Dufour, Khalaf, Bernard, and Genest (2004) doi:10.1016/j.jeconom.2003.10.024, Evans and King (1985) doi:10.1016/0304-4076(85)90085-5 and Evans and King (1988) doi:10.1016/0304-4076(88)90006-1, Glejser (1969) doi:10.1080/01621459.1969.10500976 as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) doi:10.1080/07474939908800438, Goldfeld and Quandt (1965) doi:10.1080/01621459.1965.10480811, Harrison and McCabe (1979) doi:10.1080/01621459.1979.10482544, Harvey (1976) doi:10.2307/1913974, Honda (1989) doi:10.1111/j.2517-6161.1989.tb01749.x, Horn (1981) doi:10.1080/03610928108828074, Li and Yao (2019) doi:10.1016/j.ecosta.2018.01.001 with and without the modification of Bai, Pan, and Yin (2016) doi:10.1007/s11749-017-0575-x, Rackauskas and Zuokas (2007) doi:10.1007/s10986-007-0018-6, Simonoff and Tsai (1994) doi:10.2307/2986026 with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) doi:10.1016/S0378-3758(03)00210-6, Szroeter (1978) doi:10.2307/1913831, Verbyla (1993) doi:10.1111/j.2517-6161.1993.tb01918.x, White (1980) doi:10.2307/1912934, Wilcox and Keselman (2006) doi:10.1080/10629360500107923, Yuce (2008) https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070, and Zhou, Song, and Thompson (2015) doi:10.1002/cjs.11252. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) doi:10.1080/01621459.1965.10480851, the conditional two-sided p-values of Kulinskaya (2008) doi:10.48550/arXiv.0810.2124, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) doi:10.2307/1912934, MacKinnon and White (1985) doi:10.1016/0304-4076(85)90158-7, Cribari-Neto (2004) doi:10.1016/S0167-9473(02)00366-3, Cribari-Neto et al. (2007) doi:10.1080/03610920601126589, Cribari-Neto and da Silva (2011) doi:10.1007/s10182-010-0141-2, Aftab and Chang (2016) doi:10.18187/pjsor.v12i2.983, and Li et al. (2017) doi:10.1080/00949655.2016.1198906.

Install

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

install.packages("skedastic", 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("tjfarrar/skedastic")

Development version from tjfarrar/skedastic. 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-skedastic

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-skedastic

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-skedastic

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: 4.8 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-04
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK13 OK2026-05-02

Show 2 earlier changesHide 2 earlier changes

ERROR11 OK, 1 ERROR2026-04-25
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 2.0.3

READMEYes · 491 wordsVignettesNoNEWSYes · 33% structured · does not mention 2.0.3Citation fileYes · software entry onlyWebsitenot declared in DESCRIPTION

Help pages: 44
Examples that run: 100%42 run in a plain check
Exported functions with examples: 100%
Documented parameters: 99%
Return-value docs: 100%
Help pages citing references: 95% with a bibliography file

How these are measured

Downloads

25.3K

CRAN downloads in the past year

Rank #2,984 · ~69/day · ~2.1K/mo

Releases R versions

Daily download trend is not available in this view yet.

2.3K30 days

4.5K90 days

25.3K1 year

Compare downloads with other packages →

Also on120 r2u7 autocran106 c2d4u

How downloads are counted

Repository

tjfarrar/skedastic ↗Account: tjfarrar

7Stars

0Forks

1Open issues

0Open PRs

0Releases

88Commits

2Commit identities

88 commits · Last activity 2025-06-07

Responsiveness

How these are sampled

PRs merged (all time): not measured
Median days to close, 50 most recently updated closed issues: 4
Median days to close, 50 most recently updated closed PRs: not measured

Stars over time

2024-01-23 · 62026-07-07 · 7

How these are measured

PracticesBeta

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

Checks run against github.com/tjfarrar/skedastic on 2026-10-11.

Continuous integration (1)

Travis CI (config file)

CRAN release process (1)

cran-comments.md

Lint, format, editor (1)

RStudio project

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

Dependencies

Declared dependencies

32 external dependencies (excludes base and recommended)

Installing skedastic adds 82 packages (46 with compiled code, 54 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.

Depends (1)

R >= 3.6.0

Imports (16)

Rdpack broom pracma CompQuadFormMASSquadprog inflection Rfast caretMatrixquadprogXT slam ROI osqpmgcvROI.plugin.qpoases

LinkingTo (0)

none

Suggests (19)

knitr rmarkdown devtools lmtest car tseries tibble testthat mlbench expm arrangements quantreg gmp Rmpfr cubature mvtnorm lmboot sandwich cmna

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Suggested by 1

Suggests, most downloaded first

KOTORY

How these are counted

Code & Tests

Datasets

People & History

People (2)

Thomas Farrar

Author, Maintainer since 0.1.0 (2020)

University of the Western Cape

Copyright holder

Releases (8)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
unarchivedReturned to CRAN
2025-06-08
2.0.3Latest
record
2025-06-08 · current release · diff ↗
archivedRemoved from CRAN
2025-06-02
requires archived package 'bazar'
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
2.0.2
record
2024-01-08 · diff ↗
R
R 4.3.0 released · 2023-04-21
2.0.1
record
2022-11-06 · diff ↗ ·
removed 1 export
white_lm

added 9 exports
GSS, alvm.fit, anlvm.fit, avm.ci, avm.fwls, avm.vcov, bootlm, hccme, white
R
R 4.2.0 released · 2022-04-22
1.0.4
record
2022-02-22 · diff ↗
unarchivedReturned to CRAN
2021-06-14
1.0.3
record
2021-06-14 · diff ↗ ·
added 2 exports
harrison_mccabe, yuce
archivedRemoved from CRAN
2021-06-08
check issues were not corrected in time
R
R 4.1.0 released · 2021-05-18
1.0.1
record
2020-08-28 · diff ↗

Show 4 earlier eventsHide 4 earlier events

1.0.0
record
2020-08-11 · diff ↗ ·
added 19 exports
anscombe, bickel, carapeto_holt, dDtrend, diblasi_bowman, dufour_etal, evans_king, godfrey_orme, honda, horn, pDtrend, pRQF, rackauskas_zuokas, simonoff_tsai, szroeter, twosidedpval, verbyla, wilcox_keselman, zhou_etal
R
R 4.0.0 released · 2020-04-24
0.1.0
record
2020-01-10
R
R 3.6.0 released · 2019-04-26

Package metadata

First published: 2020-01-10
Total releases: 8 / 6 yrs
License: MIT + file LICENSE OSI
Minimum R: ≥ 3.6.0
Bundled data: 3.4 MB / 2 files
Download size: 3.8 MB

Cite

Cite this package

Run in R for the authors' entry:

citation("skedastic")

The package provides a citation file, so citation() returns the authors' own entry.

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 skedastic version 2.0.3 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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