The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

mlmodels: Maximum Likelihood Models and Tools for Estimation, Prediction, and Testing

Provides a collection of maximum likelihood estimators with a consistent S3 interface. Supported models include Gaussian (linear and log-normal), logit, probit, Poisson, negative binomial (NB1 and NB2), gamma, and beta regression. A distinctive feature is flexible modeling of the scale parameter (variance, dispersion, precision, or shape) alongside the location/mean parameters. The package offers unified predict() methods, multiple variance-covariance estimators (observed information, outer product of gradients, robust/Huber-White, cluster-robust, bootstrap, jackknife), and a full suite of hypothesis tests (Wald, likelihood ratio, information matrix, Vuong, overdispersion, and goodness-of-fit). It is fully compatible with 'marginaleffects' for post-estimation analysis. Methods implemented include Cameron and Trivedi (1990) <doi:10.1016/0304-4076(90)90014-K>, for Poisson overdispersion testing, Manjon and Martinez (2014) <doi:10.1177/1536867X1401400406>, for goodness-of-fit testing of count data models, Vuong (1989) <doi:10.2307/1912557>, for non-nested likelihood ratio testing, and White (1982) <doi:10.2307/1912526>, for information matrix tests.

Version: 0.1.2
Depends: R (≥ 4.1.0)
Imports: cli, hardhat, insight, marginaleffects, MASS, matrixcalc, maxLik, rlang, tibble
Suggests: boot, dplyr, e1071, ggplot2, knitr, patchwork, pkgdown, rmarkdown, testthat (≥ 3.0.0), wooldridge
Published: 2026-05-08
DOI: 10.32614/CRAN.package.mlmodels (may not be active yet)
Author: Alfonso Sanchez-Penalver ORCID iD [aut, cre]
Maintainer: Alfonso Sanchez-Penalver <oneiros_spain at yahoo.com>
BugReports: https://github.com/alfisankipan/mlmodels/issues
License: MIT + file LICENSE
URL: https://alfisankipan.github.io/mlmodels/
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: mlmodels results

Documentation:

Reference manual: mlmodels.html , mlmodels.pdf
Vignettes: Maximum Likelihood Models in R (source, R code)
Introduction to Count Data (source, R code)
Diagnostic Tools in 'mlmodels' (source, R code)
Fractional Response Outcomes (source, R code)
Gamma versus Lognormal (source, R code)
Predictions with 'mlmodels' (source, R code)
Variance-Covariance Estimation in 'mlmodels' (source, R code)

Downloads:

Package source: mlmodels_0.1.2.tar.gz
Windows binaries: r-devel: mlmodels_0.1.2.zip, r-release: not available, r-oldrel: mlmodels_0.1.2.zip
macOS binaries: r-release (arm64): mlmodels_0.1.2.tgz, r-oldrel (arm64): mlmodels_0.1.2.tgz, r-release (x86_64): mlmodels_0.1.2.tgz, r-oldrel (x86_64): mlmodels_0.1.2.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mlmodels to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.