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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "mlmodels" in publications use:'
type: software
license: MIT
title: 'mlmodels: Maximum Likelihood Models and Tools for Estimation, Prediction,
  and Testing'
version: 0.1.2
abstract: 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)
  <https://doi.org/10.1016/0304-4076(90)90014-K>, for Poisson overdispersion testing,
  Manjon and Martinez (2014) <https://doi.org/10.1177/1536867X1401400406>, for goodness-of-fit
  testing of count data models, Vuong (1989) <https://doi.org/10.2307/1912557>, for
  non-nested likelihood ratio testing, and White (1982) <https://doi.org/10.2307/1912526>,
  for information matrix tests.
authors:
- family-names: Sanchez-Penalver
  given-names: Alfonso
  email: oneiros_spain@yahoo.com
  orcid: https://orcid.org/0000-0001-8491-4632
repository: https://cran.r-universe.dev
repository-code: https://github.com/alfisankipan/mlmodels
commit: ff1213a47d8c7abb769b80195273490458c8ed13
url: https://alfisankipan.github.io/mlmodels/
date-released: '2026-05-08'
contact:
- family-names: Sanchez-Penalver
  given-names: Alfonso
  email: oneiros_spain@yahoo.com
  orcid: https://orcid.org/0000-0001-8491-4632