{ "_id": "6a105d51acfb0bcc41ca4a14", "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", "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", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-08 19:19:03 UTC", "User": "root" }, "Author": "Alfonso Sanchez-Penalver [aut, cre] (ORCID:\n)", "Maintainer": "Alfonso Sanchez-Penalver ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-05-08 17:47:52 UTC", "RemoteUrl": "https://github.com/cran/mlmodels", "RemoteRef": "HEAD", "RemoteSha": "ff1213a47d8c7abb769b80195273490458c8ed13", "MD5sum": "f99877241153ae6415f2aeaf3af34709", "_user": "cran", "_type": "src", "_file": "mlmodels_0.1.2.tar.gz", "_fileid": "7387ac50a539c20b1aeab9935c113d9f0071d06da6d502818f2a4c238092d5d7", "_filesize": 1787507, "_sha256": "7387ac50a539c20b1aeab9935c113d9f0071d06da6d502818f2a4c238092d5d7", "_created": "2026-05-08T19:19:03.000Z", "_published": "2026-05-22T13:42:41.630Z", "_distro": "noble", "_jobs": [ { "job": 77391640301, "time": 302, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6887075738" }, { "job": 77391640012, "time": 300, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "6887073044" }, { "job": 77391639132, "time": 263, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6886980048" }, { "job": 77391638933, "time": 122, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7161959393" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/25574608789", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/mlmodels", "_commit": { "id": "ff1213a47d8c7abb769b80195273490458c8ed13", "author": "Alfonso Sanchez-Penalver ", "committer": "cran-robot ", "message": "version 0.1.2\n", "time": 1778262472 }, "_maintainer": { "name": "Alfonso Sanchez-Penalver", "email": "oneiros_spain@yahoo.com", "login": "alfisankipan", "uuid": 85194589, "orcid": "0000-0001-8491-4632" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "cli", "role": "Imports" }, { "package": "hardhat", "role": "Imports" }, { "package": "insight", "role": "Imports" }, { "package": "marginaleffects", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "matrixcalc", "role": "Imports" }, { "package": "maxLik", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "boot", "role": "Suggests" }, { "package": "dplyr", "role": "Suggests" }, { "package": "e1071", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "patchwork", "role": "Suggests" }, { "package": "pkgdown", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "wooldridge", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2026-19", "n": 1 } ], "_tags": [ { "name": "0.1.2", "date": "2026-05-08" } ], "_stars": 0, "_contributors": [ { "user": "alfisankipan", "count": 1, "uuid": 85194589 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 0, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/mlmodels" }, "_devurl": "https://github.com/alfisankipan/mlmodels", "_pkgdown": "https://alfisankipan.github.io/mlmodels/", "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/mlmodels.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_cranurl": false, "_releases": [ { "version": "0.1.2", "date": "2026-05-08" } ], "_exports": [ "%||%", "find_variables.mlmodel", "get_modeldata.mlmodel", "GOFtest", "gradientObs", "hessianObs", "IMtest", "loglikeObs", "lrtest", "ml_beta", "ml_gamma", "ml_lm", "ml_logit", "ml_negbin", "ml_poisson", "ml_probit", "OVDtest", "se", "vuongtest", "waldtest" ], "_datasets": [ { "name": "docvis", "title": "U.S. Medical Expenditure Panel Survey", "object": "docvis", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "offer", "ssiratio", "age", "educyr", "physician", "nonphysician", "medicaid", "private", "female", "phylim", "actlim", "income", "totchr", "insured", "age2", "linc", "bh", "docvis", "ldocvis", "ldocvisa", "one", "docbin" ], "rows": 3677, "table": true, "tojson": true }, { "name": "mroz", "title": "University of Michigan Panel Study of Income Dynamics (PSID)", "object": "mroz", "class": [ "data.frame" ], "fields": [ "inlf", "hours", "kidslt6", "kidsge6", "age", "educ", "wage", "repwage", "hushrs", "husage", "huseduc", "huswage", "faminc", "mtr", "motheduc", "fatheduc", "unem", "city", "exper", "nwifeinc", "lwage", "expersq" ], "rows": 753, "table": true, "tojson": true }, { "name": "pw401k", "title": "401(k) Participation Rates", "object": "pw401k", "class": [ "spec_tbl_df", "tbl_df", "tbl", "data.frame" ], "fields": [ "prate", "mrate", "totemp", "age", "sole" ], "rows": 4734, "table": true, "tojson": true }, { "name": "smoke", "title": "1979 National Health Interview Survey", "object": "smoke", "class": [ "data.frame" ], "fields": [ "educ", "cigpric", "white", "age", "income", "cigs", "restaurn", "lincome", "agesq", "lcigpric" ], "rows": 807, "table": true, "tojson": true } ], "_help": [ { "page": "AIC.mlmodel", "title": "Extract AIC from mlmodel objects", "topics": [ "AIC.mlmodel", "AIC.summary.mlmodel" ] }, { "page": "BIC.mlmodel", "title": "Extract BIC from mlmodel objects", "topics": [ "BIC.mlmodel", "BIC.summary.mlmodel" ] }, { "page": "coef.mlmodel", "title": "Extract Model Coefficients", "topics": [ "coef.mlmodel" ] }, { "page": "confint.mlmodel", "title": "Confidence Intervals for mlmodel Coefficients", "topics": [ "confint.mlmodel" ] }, { "page": "docvis", "title": "U.S. Medical Expenditure Panel Survey", "topics": [ "docvis" ] }, { "page": "find_predictors.mlmodel", "title": "Extract the predictors used in the model (for insight/marginaleffects compatibility)", "topics": [ "find_predictors.mlmodel" ] }, { "page": "find_variables.mlmodel", "title": "Extract the variables used in the model (for insight/marginaleffects compatibility)", "topics": [ "find_variables.mlmodel" ] }, { "page": "fitted.mlmodel", "title": "Extract Fitted Values from mlmodel", "topics": [ "fitted.mlmodel", "fitted.values.mlmodel" ] }, { "page": "formula.mlmodel", "title": "Extract value formula from mlmodel objects", "topics": [ "formula.mlmodel" ] }, { "page": "get_data.mlmodel", "title": "Extract data used to fit the model (for insight/marginaleffects compatibility)", "topics": [ "get_data.mlmodel", "get_modeldata.mlmodel" ] }, { "page": "GOFtest", "title": "Goodness-of-Fit Test for Count Models", "topics": [ "GOFtest", "GOFtest.mlmodel" ] }, { "page": "gradientObs", "title": "Gradient (Score) by Observation", "topics": [ "gradientObs", "gradientObs.mlmodel" ] }, { "page": "hessianObs", "title": "Hessian by Observation", "topics": [ "hessianObs", "hessianObs.mlmodel" ] }, { "page": "IMtest", "title": "Information Matrix Test for Model Misspecification", "topics": [ "IMtest", "IMtest.mlmodel" ] }, { "page": "logLik.mlmodel", "title": "Extract Log-Likelihood from mlmodel objects", "topics": [ "logLik.mlmodel", "logLik.summary.mlmodel" ] }, { "page": "loglikeObs", "title": "Log-Likelihood by Observation", "topics": [ "loglikeObs", "loglikeObs.mlmodel" ] }, { "page": "lrtest", "title": "Likelihood Ratio Test for Nested mlmodel Objects", "topics": [ "lrtest", "lrtest.mlmodel" ] }, { "page": "ml_beta", "title": "Fit Beta Model by Maximum Likelihood", "topics": [ "ml_beta" ] }, { "page": "ml_gamma", "title": "Fit Gamma Model by Maximum Likelihood", "topics": [ "ml_gamma" ] }, { "page": "ml_lm", "title": "Fit linear model by Maximum Likelihood", "topics": [ "ml_lm" ] }, { "page": "ml_logit", "title": "Fit Binary Logit Model by Maximum Likelihood", "topics": [ "ml_logit" ] }, { "page": "ml_negbin", "title": "Fit negative binomial models by Maximum Likelihood", "topics": [ "ml_negbin" ] }, { "page": "ml_poisson", "title": "Fit Poisson model by Maximum Likelihood", "topics": [ "ml_poisson" ] }, { "page": "ml_probit", "title": "Fit Binary Probit Model by Maximum Likelihood", "topics": [ "ml_probit" ] }, { "page": "mroz", "title": "University of Michigan Panel Study of Income Dynamics (PSID)", "topics": [ "mroz" ] }, { "page": "nobs.mlmodel", "title": "Extract the Number of Observations from an mlmodel", "topics": [ "nobs.mlmodel" ] }, { "page": "null-default", "title": "Null default operator", "topics": [ "%||%", "null-default" ] }, { "page": "OVDtest", "title": "Overdispersion Tests for Count Models", "topics": [ "OVDtest" ] }, { "page": "predict.mlmodel", "title": "Predictions for mlmodel models", "topics": [ "predict.mlmodel", "predict.ml_beta", "predict.ml_gamma", "predict.ml_lm", "predict.ml_logit", "predict.ml_negbin", "predict.ml_poisson", "predict.ml_probit" ] }, { "page": "pw401k", "title": "401(k) Participation Rates", "topics": [ "pw401k" ] }, { "page": "residuals.mlmodel", "title": "Extract Model Residuals", "topics": [ "residuals.mlmodel" ] }, { "page": "se", "title": "Extract Standard Errors from mlmodel Objects", "topics": [ "se", "se.mlmodel" ] }, { "page": "smoke", "title": "1979 National Health Interview Survey", "topics": [ "smoke" ] }, { "page": "summary.mlmodel", "title": "Summary for mlmodel objects", "topics": [ "summary.mlmodel", "summary.ml_beta", "summary.ml_gamma", "summary.ml_lm", "summary.ml_logit", "summary.ml_negbin", "summary.ml_poisson", "summary.ml_probit" ] }, { "page": "update.mlmodel", "title": "Update an mlmodel Call", "topics": [ "update.mlmodel", "update.ml_poisson" ] }, { "page": "vcov.mlmodel", "title": "Variance-Covariance Matrix for mlmodel Objects", "topics": [ "vcov.mlmodel" ] }, { "page": "vuongtest", "title": "Vuong's Test for Non-Nested Models", "topics": [ "vuongtest", "vuongtest.mlmodel" ] }, { "page": "waldtest", "title": "Wald Test for Linear Restrictions", "topics": [ "waldtest", "waldtest.mlmodel" ] } ], "_pkglogo": "https://github.com/cran/mlmodels/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/cran/mlmodels/raw/HEAD/README.md", "_rundeps": [ "backports", "checkmate", "cli", "data.table", "digest", "Formula", "generics", "glue", "hardhat", "insight", "lattice", "lifecycle", "magrittr", "marginaleffects", "MASS", "matrixcalc", "maxLik", "miscTools", "pillar", "pkgconfig", "rlang", "sandwich", "sparsevctrs", "tibble", "utf8", "vctrs", "zoo" ], "_vignettes": [ { "source": "mlmodels-diagnostics.Rmd", "filename": "mlmodels-diagnostics.html", "title": "Diagnostic Tools in mlmodels", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Information Matrix Test", "Implementing the Test", "Illustrating the Size and Power Problems", "Testing Nested Models", "Likelihood-ratio Test", "Wald Test", "Testing Non-nested Models", "Tests for Count Data Models", "Concluding Remarks", "References" ], "created": "2026-05-08 17:47:52", "modified": "2026-05-08 17:47:52", "commits": 1 }, { "source": "mlmodels-fractional.Rmd", "filename": "mlmodels-fractional.html", "title": "Fractional Response Outcomes", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "QMLE with Observations at the Boundary", "Estimation with Logit and Probit", "Predicted Probabilities and Marginal Effects", "Strict Fractional Responses", "Note on the Log-Likelihood of the Beta Model", "Concluding Remarks", "References" ], "created": "2026-05-08 17:47:52", "modified": "2026-05-08 17:47:52", "commits": 1 }, { "source": "mlmodels-gamma-lognormal.Rmd", "filename": "mlmodels-gamma-lognormal.html", "title": "Gamma versus Lognormal", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Exploring the Outcome Variable", "Model Estimation", "Estimates", "Postestimation Analysis", "Analysis of the Mean Predictions", "Analysis of the Predictions of the Variance", "Concluding Remarks" ], "created": "2026-05-08 17:47:52", "modified": "2026-05-08 17:47:52", "commits": 1 }, { "source": "mlmodels-countintro.Rmd", "filename": "mlmodels-countintro.html", "title": "Introduction to Count Data", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "The Data", "Poisson Model", "Test of Overdispersion", "Average Marginal Effects", "Negative Binomial Estimation", "Predictions of the Mean: Poisson vs Negative Binomial", "Model Comparison: NB1 versus NB2", "Comparing Predicted Probabilities", "Heteroskedastic Negative Binomial Models", "Concluding Remarks", "References" ], "created": "2026-05-08 17:47:52", "modified": "2026-05-08 17:47:52", "commits": 1 }, { "source": "mlmodels-basics.Rmd", "filename": "mlmodels-basics.html", "title": "Maximum Likelihood Models in R", "engine": "knitr::rmarkdown", "headings": [ "Core Features", "Quick Start", "Making Predictions", "Modeling Heteroskedasticity", "Controlling Intercepts", "Binary Outcomes: Logit", "Where to Go Next", "References" ], "created": "2026-05-08 17:47:52", "modified": "2026-05-08 17:47:52", "commits": 1 }, { "source": "mlmodels-predictions.Rmd", "filename": "mlmodels-predictions.html", "title": "Predictions with mlmodels", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "The predict() Method", "Basic Usage", "Predicting for Different Models", "Comparison with predictions() from marginaleffects", "In-Sample and Out-of-Sample Predictions", "Bootstrap Inference", "Interaction Effects and Plotting", "Concluding Remarks" ], "created": "2026-05-08 17:47:52", "modified": "2026-05-08 17:47:52", "commits": 1 }, { "source": "mlmodels-variance.Rmd", "filename": "mlmodels-variance.html", "title": "Variance-Covariance Estimation in mlmodels", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Why Different Variance Estimators Matter", "Classical Information Matrix Estimators", "Observed Information Matrix (oim) — Default", "Outer Product of Gradients (opg)", "The \"Hessian may be singular\" warning", "Example: Heteroskedastic Logit Model", "Robust Variance Estimators", "Robust (Sandwich / Huber-White) Estimator", "Clustering", "Bootstrap and Jackknife Estimators", "Why use resampling methods?", "Asymptotic Equivalence", "Important Feature of Our Implementation", "Practical Recommendations", "Quick Decision Guide", "Best Practices", "Concluding Remarks" ], "created": "2026-05-08 17:47:52", "modified": "2026-05-08 17:47:52", "commits": 1 } ], "_score": 3.5440680443502757, "_indexed": true, "_nocasepkg": "mlmodels", "_universes": [ "cran", "alfisankipan" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.1.2", "date": "2026-05-08T19:21:41.000Z", "distro": "noble", "commit": "ff1213a47d8c7abb769b80195273490458c8ed13", "fileid": "a0dd09d194283d4a08a53bb2a73e276f2f75bd71d7beff85733cc25730cfcf0f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25574608789" }, { "r": "4.6.0", "os": "linux", "version": "0.1.2", "date": "2026-05-08T19:21:30.000Z", "distro": "noble", "commit": "ff1213a47d8c7abb769b80195273490458c8ed13", "fileid": "0ecd7831690d3a4d30931fb402ebfa08d7eb0c9f0fd2a6b3d174459ebc14a40e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/25574608789" }, { "r": "4.6.0", "os": "wasm", "version": "0.1.2", "date": "2026-05-22T13:42:21.000Z", "commit": "ff1213a47d8c7abb769b80195273490458c8ed13", "fileid": "6a9e31cac20d06ebb5f05c08c3c07159fa036d7105ad1eb3492eaa3c691a4040", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/25574608789" } ] }