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cff-version: 1.2.0
message: 'To cite package "GLAMMGoF" in publications use:'
type: software
license: MIT
title: 'GLAMMGoF: Resampling-Based Predictive Validation for Generalized Linear and
  Generalized Additive Models'
version: 1.0.7
doi: 10.32614/CRAN.package.GLAMMGoF
abstract: Provides resampling-based predictive validation for generalized linear and
  generalized additive models (with or without random effects) fitted using packages
  such as glmmTMB, mgcv, stats, lme4, and MASS. Predictive performance is assessed
  using either repeated random holdout (Monte Carlo cross-validation) or bootstrap
  resampling with out-of-bag evaluation. In each replicate, models are refit to a
  training dataset and evaluated on separate testing data, generating sampling distributions
  of in-sample and out-of-sample performance statistics. For continuous or integer
  response models, supported metrics include relative root mean squared error (RRMSE),
  relative mean absolute error (RMAE), relative median absolute error (RMedAE), and
  relative bias (RBIAS). For binary response models, supported metrics include AUC,
  Brier score, and log loss. All predictive metrics are based on population-level
  predictions, meaning random effects are excluded when present. Optional residual
  diagnostics can also be performed using the DHARMa package.
authors:
- family-names: Shea
  given-names: Colin
  email: colin.shea@myfwc.com
  orcid: https://orcid.org/0000-0002-2906-250X
repository: https://colinpshea.r-universe.dev
repository-code: https://github.com/colinpshea/glammgof
commit: 53c63557570915baf9e7f1016cbaca15f3029877
url: https://github.com/colinpshea/glammgof
date-released: '2026-05-14'
contact:
- family-names: Shea
  given-names: Colin
  email: colin.shea@myfwc.com
  orcid: https://orcid.org/0000-0002-2906-250X