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: not available, r-release: not available, r-oldrel: not available
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

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