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.

See also

Useful links:

• https://alfisankipan.github.io/mlmodels/

• Report bugs at https://github.com/alfisankipan/mlmodels/issues

Author

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