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). It is fully compatible with marginaleffects for post-estimation analysis.

Author

Maintainer: Alfonso Sanchez-Penalver oneiros_spain@yahoo.com (ORCID)