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    package is found in the functions that implement the as-yet-unpublished 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>.