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)
<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) <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: |
1.0.4 |
| 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), bazar (≥ 1.0.11), expm (≥ 0.999.4), data.table (≥ 1.12.8), mvtnorm (≥ 1.1.0) |
| Suggests: |
knitr, rmarkdown, devtools, lmtest, car, tseries, tibble, testthat, mlbench |
| Published: |
2022-02-22 |
| Author: |
Thomas Farrar 
[aut, cre],
University of the Western Cape [cph] |
| Maintainer: |
Thomas Farrar <tjfarrar at alumni.uwaterloo.ca> |
| BugReports: |
https://github.com/tjfarrar/skedastic/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/tjfarrar/skedastic |
| NeedsCompilation: |
no |
| Citation: |
skedastic citation info |
| Materials: |
README NEWS |
| CRAN checks: |
skedastic results |
Documentation:
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