- introductions
- course material: https://github.com/bbolker/quebec_2019 and https://bbolker.github.io/quebec_2019
- data sets
- please ask questions!

## focus

- "advanced" end users (modelers, not statisticians)
- mechanistic rather than phenomenological models (cf. @breiman_statistical_2001)
- work with packages when possible ...
- ... but ...

<!-- or: <div style="width:300px; height:200px"> ... </div> -->
![](pix/dracones-768x384.jpg){width=250px}

[Rochester Digital Scholarship lab](https://dslab.lib.rochester.edu/hunt-lenox-globe/)

- things used to be simple (`lm`, `glm`, `nlme`, `lme4` ...)
- now "let a hundred flowers bloom" (`TMB`, `brms`, `greta`, NIMBLE, ...)

![](pix/Chinese-poster-flowers-smaller.jpg) [Dan Jeffries](http://meuploads.com/2014/08/28/let-hundred-flowers-bloom/)

## outline

- likelihood estimation and inference
- nonlinear optimization methods
- simple MLE examples
- latent variables/random effects in nonlinear models
- (extended generalized linear mixed models)
- complex examples/nonlinear mixed models

![](pix/models-glmm.png)


## tentative schedule

- 0830-1000: review of likelihood/inference, optimization methods, parameterization (from a geometric perspective)
- 1000-1030: break
- 1030-1200: simple examples with `bbmle`/`optim`
- 1200-1330: lunch
- 1330-1500: latent/mixed models
- 1500-1530: break
- 1530-1700: advanced examples with `nlmer`/`TMB`/etc.

## references

