| Day 1 | morning | overview: statistical analysis/modeling of ecological data; likelihood inference (student introductions) |
| afternoon | Introduction to R. Working on the command line; importing data; infrastructure (help system, mailing lists, add-on packages, development environments) |
|
| Day 2 | morning | deterministic modeling components. characterizing behavior of functions: derivatives and limits. (more on student data?) |
| afternoon | data manipulation and exploratory data analysis |
|
| Day 3 | morning | stochastic modeling components. probability basics, probability distributions. |
| afternoon | exploring probability distributions. Simple stochastic simulations. (project proposals) |
|
| Day 4 | morning | likelihood inference |
| afternoon | likelihood examples |
|
| Day 5 | morning | optimization techniques: Nelder-Mead simplex, gradient methods, simulated annealing |
| afternoon | examples – diagnosing and troubleshooting optimization problems |
|
| Week 2 | class projects, in groups of 4-5. Replication of existing analyses in peer-reviewed papers, or novel analyses of student data sets/questions. Lectures on advanced techniques, based on student interest: possibilities include classical statistics in R (linear models, GLMs, survival analysis); spatial analysis; mixed models including GLMMs; bootstrapping; etc. |