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  "Title": "Goodness-of-Fit Tests for Univariate Data via Energy",
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  "Authors@R": "person(\"John\", \"Haman\", email = \"mail@johnhaman.org\", role = c(\"aut\", \"cre\"))",
  "Description": "Conduct one- and two-sample goodness-of-fit tests for\nunivariate data. In the one-sample case, normal, uniform,\nexponential, Bernoulli, binomial, geometric, beta, Poisson,\nlognormal, Laplace, asymmetric Laplace, inverse Gaussian,\nhalf-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto\ndistributions are supported. egof.test() can also test\ngoodness-of-fit to any distribution with a continuous\ndistribution function. A subset of the available distributions\ncan be tested for the composite goodness-of-fit hypothesis,\nthat is, one can test for distribution fit with unknown\nparameters. P-values are calculated via parametric bootstrap.",
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  "Encoding": "UTF-8",
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  "Repository": "https://jthaman.r-universe.dev",
  "Date/Publication": "2026-05-12 19:05:49 UTC",
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