Glmulti

glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models. We introduce glmulti, an R package for automated model selection and multi-model inference with glm and related functions. From a list of explanatory variables, the provided function glmulti builds all possible unique models involving these variables and, optionally, their pairwise interactions. Restrictions can be specified for candidate models, by excluding specific terms, enforcing marginality, or controlling model complexity. Models are fitted with standard R functions like glm. The n best models and their support (e.g., (Q)AIC, (Q)AICc, or BIC) are returned, allowing model selection and multi-model inference through standard R functions. The package is optimized for large candidate sets by avoiding memory limitation, facilitating parallelization and providing, in addition to exhaustive screening, a compiled genetic algorithm method. This article briefly presents the statistical framework and introduces the package, with applications to simulated and real data.

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang: abess: A Fast Best Subset Selection Library in Python and R (2021) arXiv
  2. Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang: BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models (2020) not zbMATH
  3. Kabaila, Paul; Welsh, A. H.; Wijethunga, Christeen: Finite sample properties of confidence intervals centered on a model averaged estimator (2020)
  4. Marc Hofmann, Cristian Gatu, Erricos J. Kontoghiorghes, Ana Colubi, Achim Zeileis: lmSubsets: Exact Variable-Subset Selection in Linear Regression for R (2020) not zbMATH
  5. Dunder, Emre; Gumustekin, Serpil; Cengiz, Mehmet Ali: Variable selection in gamma regression models via artificial bee colony algorithm (2018)
  6. Garth Tarr; Samuel Müller; Alan Welsh: mplot: An R Package for Graphical Model Stability and Variable Selection Procedures (2018) not zbMATH
  7. Koç, Haydar; Dünder, Emre; Gümüştekin, Serpil; Koç, Tuba; Cengiz, Mehmet Ali: Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria (2018)
  8. Stoklosa, Jakub; Warton, David I.: A generalized estimating equation approach to multivariate adaptive regression splines (2018)
  9. Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang: BeSS: An R Package for Best Subset Selection in Linear, Logistic and CoxPH Models (2017) arXiv
  10. Razieh Nabi Abdolyousefi, Xiaogang Su: coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models (2016) arXiv
  11. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  12. Thomas Grubinger; Achim Zeileis; Karl-Peter Pfeiffer: evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R (2014) not zbMATH
  13. Luca Scrucca: GA: A Package for Genetic Algorithms in R (2013) not zbMATH
  14. Ji, Yonggang; Lin, Nan; Zhang, Baoxue: Model selection in binary and Tobit quantile regression using the Gibbs sampler (2012)
  15. Vincent Calcagno; Claire de Mazancourt: glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models (2010) not zbMATH