mlmodels
0.1.2Maximum Likelihood Models and Tools for Estimation, Prediction, and Testing
Overview
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.
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- OK2026-05-097 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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People & History
1 release. R releases are shown for context.
- archivedRemoved from CRAN2026-06-12issues were not corrected in time
- 0.1.22026-05-08
- RR 4.6.0 released · 2026-04-24
Package metadata
- Total releases
- 1
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 156 KB / 4 files
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