{ "_id": "6a103aabacfb0bcc41c9b430", "Package": "mlmodels", "Title": "Maximum Likelihood Models and Tools for Estimation, Prediction,\nand Testing", "Version": "0.1.2", "Authors@R": "person(given = \"Alfonso\",\nfamily = \"Sanchez-Penalver\",\nemail = \"oneiros_spain@yahoo.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-8491-4632\"))", "Description": "Provides a collection of maximum likelihood estimators\nwith a consistent S3 interface. Supported models include\nGaussian (linear and log-normal), logit, probit, Poisson,\nnegative binomial (NB1 and NB2), gamma, and beta regression. A\ndistinctive feature is flexible modeling of the scale parameter\n(variance, dispersion, precision, or shape) alongside the\nlocation/mean parameters. The package offers unified predict()\nmethods, multiple variance-covariance estimators (observed\ninformation, outer product of gradients, robust/Huber-White,\ncluster-robust, bootstrap, jackknife), and a full suite of\nhypothesis tests (Wald, likelihood ratio, information matrix,\nVuong, overdispersion, and goodness-of-fit). It is fully\ncompatible with 'marginaleffects' for post-estimation analysis.\nMethods implemented include Cameron and Trivedi (1990)\n, for Poisson overdispersion\ntesting, Manjon and Martinez (2014)\n, for goodness-of-fit testing\nof count data models, Vuong (1989) , for\nnon-nested likelihood ratio testing, and White (1982)\n, for information matrix tests.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/pkgdown/logo": "man/figures/logo.png", "URL": "https://alfisankipan.github.io/mlmodels/", "BugReports": "https://github.com/alfisankipan/mlmodels/issues", "Repository": "https://alfisankipan.r-universe.dev", "Date/Publication": "2026-05-11 21:06:28 UTC", "RemoteUrl": "https://github.com/alfisankipan/mlmodels", "RemoteRef": "HEAD", "RemoteSha": "a4c5abdc2f00e7c0c4edd35400ae16b476ea499c", "NeedsCompilation": "no", "Packaged": { 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