Beginner's Guide to Zero-Inflated Models with R

This book provides the statistical tools to aid analysis of datasets. It deals with two main difficulties faced with large datasets, lots of zeros and dependency.

- 1. Introduction
- 2. Essential distributions for zero-inflated models
- 3. Introducing zero-inflated Poisson models
- 4. Zero-inflated models applied to orange-crowned warblers
- 5. Zero-inflated models applied to shark abundance data
- 6. Hurdle models for riparian spider counts
- 7. Models for zero-inflated continuous data applied to Chinese tallow trees
- 8. Linear mixed effects models
- 9. Zero-altered models with two-way nested and crossed random effects
- 10. Introduction to Bayesian statistics
- 11. Bayesian analysis for Poisson, NB, ZIP and Bernoulli models
- 12. Bayesian analysis for linear mixed effects models
- Beaver and Lilies
- 13. The zero trick to fit any distribution in a Bayesian analysis
- 14. Bayesian model selection techniques
- 15. Bayesian model selection applied to zero-inflated butterfly data
- 16. Zero-inflated seagrass coverage data
- 17. Other distributions
- 18. Multivariate GLMM