Schedule: Monday 6 Sept 9-9:15 Welcome to all participants in SMLP (Zoom link will be provided) 9:15-11 lecture chapter 1 (Shravan) 11-11:30 Break 11:30-12:30 Shravan lecture+exercises/demos 12:30-1:30 Lunch 1:30-3:30 Time for exercises for the students (Anna + Paula available for questions) 3:30-5:00 Anna will present logit transform in preparation for a later chapter 5-6PM Social hour (link will be provided) Tuesday 7 Sept 9-11 HW solutions + lecture chapter 2 (Shravan) 11-11:30 Break 11:30-12:30 Shravan lecture+exercises/demos 12:30-1:30 Lunch 1:30-3:30 Time for HW exercises for the students (Shravan + Anna + Paula available for questions) 3:30-5:00 Spillover time (Shravan) 5:00-6:00 PM Talk by Dora Matzke (zoom link will be provided) Wednesday 8 Sept 9-11 HW solutions + lecture chapter 3 (Shravan) 11-11:30 Break 11:30-12:30 Shravan lecture+exercises/demos 12:30-1:30 Lunch 1:30-3:30 Time for exercises for the students (Anna + Paula available for questions) 3:30-5:00 Anna will present example analyses 5-6PM Social hour (link will be provided) Thursday 9 Sept 9-11 HW solutions + lecture chapter 4 (Shravan) 11-11:30 Break 11:30-12:30 Shravan lecture+exercises/demos 12:30-1:30 Lunch 1:30-3:30 Time for HW exercises for the students (Shravan + Anna + Paula available for questions) 3:30-5:00 Anna will present example analyses (HW solutions will be released) 5:00-6:00 PM Talk by Phillip Alday (zoom link will be provided) Friday 10 Sept 9-11 HW solutions + lecture chapters 5+6 (Shravan) 11-11:30 Break 11:30-12:30 Shravan lecture+exercises/demos 12:30-1:30 Lunch 1:30-3:30 Time for exercises for the students (Anna + Paula available for questions) 3:30-5:00 Anna will talk about R Markdown and developing a reproducible workflow (45 mins); time for free discussion (45 mins) 5-5:30 farewell and wrap-up (link will be provided)

We also assume familiarity with R. Participants will benefit most if they have previously fit linear models and linear mixed models (using lme4) in R, in any scientific domain within linguistics and psychology. No knowledge of calculus or linear algebra is assumed (but will be helpful to know), but basic school level mathematics knowledge is assumed (this will be quickly revisited in class).

Install the library bcogsci from here.

- Day 1: HW: Exercises 1.2, 1.4, 1.6, 1.8
- Day 2: HW: Exercises 2.1, 2.2, 2.5
- Day 3: HW: Exercises 3.1., 3.2, 3.3, Optional: 3.4
- Day 4: HW: Exercises 4.2., 4.3, 4.4
- Day 5: HW: Exercises 5.1., 5.2, 5.4, 5.5
- Video:

- Additional notes, spillover from day 4: shrinkage in LMMs
- Slides: Rmd file, pdf file.

- Video:

- brms tutorial by the author of the package, Paul Buerkner.
- Ordinal regression models in psychological research: A tutorial, by Buerkner and Vuorre.
- Contrast coding tutorial, by Schad, Hohenstein, Vasishth, Kliegl.
- Bayesian workflow tutorial, by Schad, Betancourt, Vasishth.
- Linear mixed models tutorial, Sorensen, Hohenstein, Vasishth.
- brms tutorial for phonetics/phonology, Vasishth, Nicenboim, Beckman, Li, Kong.
- Workflow Techniques for the Robust Use of Bayes Factors by Daniel J. Schad, Bruno Nicenboim, Paul-Christian Bürkner, Michael Betancourt, Shravan Vasishth.
- Sample size determination for Bayesian hierarchical models commonly used in psycholinguistics. Shravan Vasishth, Himanshu Yadav, Daniel Schad, and Bruno Nicenboim. Submitted to Computational Brain and Behavior, 2021.
- Michael Betancourt's resources: These are a must if you want to get deeper into Stan and Bayesian modeling.
- MCMC animations/visualizations,McElreath's blog post on MCMC