{ "_id": "6a214b63cd65a98ecbd2c673", "Package": "gausscov", "Version": "1.1.8", "Date": "2025-7-4", "Title": "The Gaussian Covariate Method for Variable Selection", "Authors@R": "person(\"Laurie\", \"Davies\", role = c(\"aut\",\"cre\"),email =\"pldavies44@cantab.net\")", "Author": "Laurie Davies [aut, cre]", "Maintainer": "Laurie Davies ", "Description": "The standard linear regression theory whether frequentist\nor Bayesian is based on an 'assumed (revealed?) truth' (John\nTukey) attitude to models. This is reflected in the language of\nstatistical inference which involves a concept of truth, for\nexample confidence intervals, hypothesis testing and\nconsistency. The motivation behind this package was to remove\nthe word true from the theory and practice of linear regression\nand to replace it by approximation. The approximations\nconsidered are the least squares approximations. An\napproximation is called valid if it contains no irrelevant\ncovariates. This is operationalized using the concept of a\nGaussian P-value which is the probability that pure Gaussian\nnoise is better in term of least squares than the covariate.\nThe precise definition given in the paper \"An Approximation\nBased Theory of Linear Regression\". Only four simple equations\nare required. Moreover the Gaussian P-values can be simply\nderived from standard F P-values. Furthermore they are exact\nand valid whatever the data in contrast F P-values are only\nvalid for specially designed simulations. A valid approximation\nis one where all the Gaussian P-values are less than a\nthreshold p0 specified by the statistician, in this package\nwith the default value 0.01. This approximations approach is\nnot only much simpler it is overwhelmingly better than the\nstandard model based approach. The will be demonstrated using\nhigh dimensional regression and vector autoregression real data\nsets. The goal is to find valid approximations. The search\nfunction is f1st which is a greedy forward selection procedure\nwhich results in either just one or no approximations which may\nhowever not be valid. If the size is less than than a threshold\nwith default value 21 then an all subset procedure is called\nwhich returns the best valid subset. A good default start is\nf1st(y,x,kmn=15) The best function for returning multiple\napproximations is f3st which repeatedly calls f1st. For more\ninformation see the papers: L. Davies and L. Duembgen,\n\"Covariate Selection Based on a Model-free Approach to Linear\nRegression with Exact Probabilities\",\n, L. Davies, \"An Approximation\nBased Theory of Linear Regression\", 2024,\n.", "LazyData": "true", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "6.1.1", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-04 09:52:01 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-07-09 13:00:02 UTC", "RemoteUrl": "https://github.com/cran/gausscov", "RemoteRef": "HEAD", "RemoteSha": "ecb48f9d64502780a36347bce46554988c43c583", "MD5sum": "9cc2f9c1fc5d173e44c133c05be992ab", "_user": "cran", "_type": "src", "_file": "gausscov_1.1.8.tar.gz", "_fileid": "b9d968e4d067a2f049ad0df13e76834d09579dde60458ca9dd8160e7f69cb473", "_filesize": 2829859, "_sha256": "b9d968e4d067a2f049ad0df13e76834d09579dde60458ca9dd8160e7f69cb473", "_created": "2026-06-04T09:52:01.000Z", "_published": "2026-06-04T09:54:43.540Z", "_distro": "noble", "_jobs": [ { "job": 79493696362, "time": 108, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7408423918" 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