skedastic: Heteroskedasticity Diagnostics for Linear Regression Models

Implements numerous methods for detecting heteroskedasticity (sometimes called heteroscedasticity) in the classical linear regression model. These include a test based on Anscombe (1961) <>, 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) <>, 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) <arXiv:0810.2124v1>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). Homoskedasticity refers to the assumption of constant variance that is imposed on the model errors (disturbances); heteroskedasticity is the violation of this assumption.

Version: 1.0.3
Depends: R (≥ 3.6.0)
Imports: Rdpack (≥ 0.11.1), broom (≥ 0.5.6), pracma (≥ 2.2.9), gmp (≥ 0.5.13), Rmpfr (≥ 0.8.0), arrangements (≥ 1.1.8), cubature (≥ 2.0.4), quantreg (≥ 5.55), CompQuadForm (≥ 1.4.3), MASS (≥ 7.3.47), boot (≥ 1.3.24), berryFunctions (≥ 1.17), bazar (≥ 1.0.11), expm (≥ 0.999.4), data.table (≥ 1.12.8), Hmisc (≥ 4.4.2), mvtnorm (≥ 1.1.0)
Suggests: knitr, rmarkdown, devtools, lmtest, car, tseries, tibble, testthat, mlbench
Published: 2021-06-14
Author: Thomas Farrar ORCID iD [aut, cre], University of the Western Cape [cph]
Maintainer: Thomas Farrar <tjfarrar at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: skedastic citation info
Materials: README NEWS
CRAN checks: skedastic results


Reference manual: skedastic.pdf
Package source: skedastic_1.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): skedastic_1.0.3.tgz, r-release (x86_64): skedastic_1.0.3.tgz, r-oldrel: skedastic_1.0.3.tgz
Old sources: skedastic archive


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