- Core concepts: In what ways is random sampling useful for data analysis?
- Introduction
- Why Monte Carlo methods?
- Distributions and null models
- Use cases
- Sampling from data sets: foundations
- The Jackknife
- The Bootstrap
- Randomization
- Sampling from data sets: complex designs
- Complex models
- Beyond traditional models
- Parallel processing in R
- Parallel processing with Rscript
- Sampling from data sets: decision errors and predicting new data
- False positives
- False negatives
- Cross-validation
- Simulating data for inference
- Introduction to simulation modeling
- Simulating null distributions
- Approximate Bayesian computation
- Genetic algorithm