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mlmodels: Maximum Likelihood Models for R

mlmodels provides a consistent and flexible framework for maximum likelihood estimation in R. It includes a wide range of models with a unified S3 interface, support for modeling scale parameters (heteroskedasticity), rich post-estimation tools, and excellent compatibility with the marginaleffects package.

Key Features

• Consistent interface across models: ml_lm(), ml_logit(), ml_probit(), ml_poisson(), ml_negbin(), ml_gamma(), ml_beta(), etc.
• Flexible modeling of the scale parameter (variance, dispersion, precision, or shape) alongside the mean.
• Rich predict() method with many output types (response, mean, variance, probabilities, etc.).
• Multiple variance-covariance estimators: OIM, OPG, robust, cluster-robust, bootstrap, and jackknife.
• Comprehensive hypothesis testing: Wald, likelihood ratio, information matrix, Vuong, overdispersion, and goodness-of-fit tests.
• Full compatibility with marginaleffects for marginal effects and predictions.

Installation

You can install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("alfisankipan/mlmodels")

(The package will soon be available on CRAN.)

Documentation

• Getting Started
• Count Data Models
• Diagnostic Tools
• Fractional Response Outcomes
• Gamma and Lognormal Models
• Predictions and Marginal Effects
• Variance Estimators and Inference

Quick Example

library(mlmodels)

data("mroz")

fit <- ml_logit(inlf ~ age + I(age^2) + huswage + educ + unem, 
                data = mroz)

summary(fit, vcov.type = "robust")

Acknowledgements

This package builds on the excellent maxLik package by Arne Henningsen and others for the underlying optimization engine.

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.
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