R package glmmTMB: Generalized Linear Mixed Models using Template Model Builder. Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via ’TMB’ (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Feng, Cindy: Zero-inflated models for adjusting varying exposures: a cautionary note on the pitfalls of using offset (2022)
- Sciandra, Mariangela; Spera, Irene Carola: A model-based approach to Spotify data analysis: a beta GLMM (2022)
- Alex Stringer: Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package (2021) arXiv
- Bonner, S., Kim, H.-N., Westneat, D., Mutzel, A., Wright, J., Schofield, M.: dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble (2021) not zbMATH
- Kruppa, Jochen; Hothorn, Ludwig: A comparison study on modeling of clustered and overdispersed count data for multiple comparisons (2021)
- Zheng, Nan; Cadigan, Noel: Frequentist delta-variance approximations with mixed-effects models and TMB (2021)
- Liu, Juxin; Ma, Yanyuan; Johnstone, Jill: A goodness-of-fit test for zero-inflated Poisson mixed effects models in tree abundance studies (2020)
- Colman Humphrey, Dan Swingley: Regression Analysis of Proportion Outcomes with Random Effects (2018) arXiv