{ "_id": "6a1e86dc1d7bb097a0a677f7", "Package": "skedastic", "Type": "Package", "Title": "Handling Heteroskedasticity in the Linear Regression Model", "Version": "2.0.3", "Authors@R": "c(person(given = \"Thomas\", family = \"Farrar\", \nemail = \"tjfarrar@alumni.uwaterloo.ca\", role = c(\"aut\", \"cre\"),\ncomment = c(\"0000-0003-0744-6972\")),\nperson(given = \"University of the Western Cape\", role = \"cph\"))", "Description": "Implements numerous methods for testing for, modelling,\nand correcting for heteroskedasticity in the classical linear\nregression model. The most novel contribution of the package is\nfound in the functions that implement the as-yet-unpublished\nauxiliary linear variance models and auxiliary nonlinear\nvariance models that are designed to estimate error variances\nin a heteroskedastic linear regression model. These models\nfollow principles of statistical learning described in Hastie\n(2009) . The nonlinear version\nof the model is estimated using quasi-likelihood methods as\ndescribed in Seber and Wild (2003, ISBN: 0-471-47135-6).\nBootstrap methods for approximate confidence intervals for\nerror variances are implemented as described in Efron and\nTibshirani (1993, ISBN: 978-1-4899-4541-9), including also the\nexpansion technique described in Hesterberg (2014)\n. The wild bootstrap\nemployed here follows the description in Davidson and Flachaire\n(2008) . Tuning of\nhyper-parameters makes use of a golden section search function\nthat is modelled after the MATLAB function of Zarnowiec (2022)\n.\nA methodological description of the algorithm can be found in\nFox (2021, ISBN: 978-1-003-00957-3). There are 25 different\nfunctions that implement hypothesis tests for\nheteroskedasticity. These include a test based on Anscombe\n(1961) ,\nRamsey's (1969) BAMSET Test\n, the tests of Bickel\n(1978) , Breusch and Pagan (1979)\n with and without the modification\nproposed by Koenker (1981) ,\nCarapeto and Holt (2003) ,\nCook and Weisberg (1983) (including\ntheir graphical methods), Diblasi and Bowman (1997)\n, Dufour, Khalaf, Bernard,\nand Genest (2004) , Evans\nand King (1985) and Evans\nand King (1988) , Glejser\n(1969) as formulated by\nMittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4),\nGodfrey and Orme (1999) ,\nGoldfeld and Quandt (1965)\n, Harrison and McCabe\n(1979) , Harvey (1976)\n, Honda (1989)\n, Horn (1981)\n, Li and Yao (2019)\n with and without the\nmodification of Bai, Pan, and Yin (2016)\n, Rackauskas and Zuokas (2007)\n, Simonoff and Tsai (1994)\n with and without the modification of\nFerrari, Cysneiros, and Cribari-Neto (2004)\n, Szroeter (1978)\n, Verbyla (1993)\n, White (1980)\n, Wilcox and Keselman (2006)\n, Yuce (2008)\n,\nand Zhou, Song, and Thompson (2015) .\nBesides these heteroskedasticity tests, there are supporting\nfunctions that compute the BLUS residuals of Theil (1965)\n, the conditional two-sided\np-values of Kulinskaya (2008) ,\nand probabilities for the nonparametric trend statistic of\nLehmann (1975, ISBN: 0-816-24996-1). For handling\nheteroskedasticity, in addition to the new auxiliary variance\nmodel methods, there is a function to implement various\nexisting Heteroskedasticity-Consistent Covariance Matrix\nEstimators from the literature, such as those of White (1980)\n, MacKinnon and White (1985)\n, Cribari-Neto (2004)\n, Cribari-Neto et al. (2007)\n, Cribari-Neto and da Silva\n(2011) , Aftab and Chang (2016)\n, and Li et al. (2017)\n.", "RdMacros": "Rdpack", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "URL": "https://github.com/tjfarrar/skedastic", "BugReports": "https://github.com/tjfarrar/skedastic/issues", "Config/pak/sysreqs": "make libicu-dev", "Repository": "https://tjfarrar.r-universe.dev", "Date/Publication": "2025-06-07 14:32:56 UTC", "RemoteUrl": "https://github.com/tjfarrar/skedastic", "RemoteRef": "HEAD", "RemoteSha": "a26f296182b93a61012d8fef3094741ca9e60c19", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 07:25:04 UTC", "User": "root" }, "Author": "Thomas Farrar [aut, cre] (0000-0003-0744-6972),\nUniversity of the Western Cape [cph]", "Maintainer": "Thomas Farrar ", "MD5sum": "3a910f8b5f9143633f1beec87e580dba", "_user": "tjfarrar", "_type": "src", "_file": "skedastic_2.0.3.tar.gz", "_fileid": "a0e882a010ffd6feaa9a58511b41c0b64975f1cd2b763c20c0699940c0dda707", "_filesize": 4455889, "_sha256": "a0e882a010ffd6feaa9a58511b41c0b64975f1cd2b763c20c0699940c0dda707", "_created": "2026-06-02T07:25:04.000Z", "_published": "2026-06-02T07:31:39.980Z", "_distro": "noble", "_jobs": [ { "job": 79020276805, "time": 253, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7351525696" }, { "job": 79020276806, "time": 210, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7351512593" }, { "job": 79020276905, "time": 347, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7351540832" }, { "job": 79020276798, "time": 204, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7351500983" }, { "job": 79019612971, "time": 273, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7351447647" }, { "job": 79020276748, "time": 150, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7351494367" }, { "job": 79020276964, "time": 209, "config": "windows-devel", "r": "4.7.0", "check": "OK", 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Test for Heteroskedasticity in a Linear Regression Model", "topics": [ "carapeto_holt" ] }, { "page": "cook_weisberg", "title": "Cook-Weisberg Score Test for Heteroskedasticity in a Linear Regression Model", "topics": [ "cook_weisberg" ] }, { "page": "countpeaks", "title": "Count peaks in a data sequence", "topics": [ "countpeaks" ] }, { "page": "dDtrend", "title": "Probability mass function of nonparametric trend statistic D", "topics": [ "dDtrend" ] }, { "page": "diblasi_bowman", "title": "Diblasi and Bowman's Test for Heteroskedasticity in a Linear Regression Model", "topics": [ "diblasi_bowman" ] }, { "page": "dpeak", "title": "Probability mass function of number of peaks in an i.i.d. random sequence", "topics": [ "dpeak" ] }, { "page": "dpeakdat", "title": "Probability distribution for number of peaks in a continuous, uncorrelated stochastic series", "topics": [ "dpeakdat" ] }, { "page": "dufour_etal", "title": "Dufour et al.'s Monte Carlo Test for Heteroskedasticity in a Linear 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