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>, 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>, 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.