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README.Rmd
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---
title: Statistical Rethinking colearning 2022
output:
github_document:
toc: true
---
```{r include = FALSE}
knitr::opts_chunk$set( fig.path = "graphics/" )
```
---
This repository contains resources and information for a colearning group
meeting regularly to discuss lectures and homework assignments from the
[Statistical Rethinking 2022](https://github.com/rmcelreath/stat_rethinking_2022)
course.
## Schedule
Adjusting from Richard's schedule for our pace. Note these are meeting dates
indicating when lectures, readings and homework are **assigned**, to be
discussed on/completed by the next meeting.
| Meeting date | Lectures | Reading | Homework |
|---|---|---|---|
| 2022-01-13 | [(1) The Golem of Prague](https://youtu.be/cclUd_HoRlo), [(2) Bayesian Inference](https://www.youtube.com/watch?v=guTdrfycW2Q&list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN&index=2) | Chapters 1, 2 and 3 | [Homework 1](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week01.pdf) |
| 2022-01-26 | [(3) Basic Regression](https://www.youtube.com/watch?v=zYYBtxHWE0A), [(4) Categories & Curves](https://youtu.be/QiHKdvAbYII) | Chapter 4 | [Homework 2](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week02.pdf) |
| 2022-02-11 | [(5) Confounding](https://youtu.be/UpP-_mBvECI), [(6) Even Worse Confounding](https://www.youtube.com/watch?v=NSuTaeW6Orc) | Chapters 5 and 6 | [Homework 3](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week03.pdf) |
| 2022-02-24 | [(7) Overfitting](https://www.youtube.com/watch?v=odGAAJDlgp8&list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN&index=7)| Chapter 7 | |
| 2022-03-11 | [(8) Markov Chain Monte Carlo](https://www.youtube.com/watch?v=Qqz5AJjyugM&list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN&index=8&pp=sAQB) | Chapter 8, 9 | [Homework 4](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week04.pdf) |
| 2022-03-25 | [(9) Logistic and Binomial GLMs](https://www.youtube.com/watch?v=nPi5yGbfxuo&list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN&index=9), [(10) Sensitivity and Poisson GLMs](https://www.youtube.com/watch?v=YrwL6t0kW2I) | Chapters 10, 11 | [Homework 5](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week05.pdf) |
| 2022-04-06 | [(11) Ordered Categories](https://www.youtube.com/watch?v=-397DMPooR8&list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN&index=11), [(12) Multilevel Models](https://www.youtube.com/watch?v=SocRgsf202M&list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN&index=12) | Chapters 12, 13 | [Homework 6](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week06.pdf) |
| 2022-04-22 | [(13) Multi-Multilevel Models](https://youtu.be/n2aJYtuGu54), [(14) Correlated varying effects](https://youtu.be/XDoAglqd7ss) | Chapters 13, 14 | [Homework 7](https://github.com/rmcelreath/stat_rethinking_2022/blob/main/homework/week07.pdf) |
## Resources
* Lectures: https://github.com/rmcelreath/stat_rethinking_2022#calendar--topical-outline
* Homework: https://github.com/rmcelreath/stat_rethinking_2022/tree/main/homework
Additional material using other packages or languages
* Original R: https://github.com/rmcelreath/rethinking/
* R + Tidyverse + ggplot2 + brms: https://bookdown.org/content/4857/
* Python and PyMC3: Python/PyMC3
* Julia and Turing: https://github.com/StatisticalRethinkingJulia and https://github.com/StatisticalRethinkingJulia/TuringModels.jl
See Richard's comments about these here: https://github.com/rmcelreath/stat_rethinking_2022#original-r-flavor
Also, Alec's notes and solutions of the 2019 material: https://github.com/robitalec/statistical-rethinking and https://www.statistical-rethinking.robitalec.ca/
## Installation
Package specific install directions. We'll update these as we go!
Rethinking
* [`rethinking`](https://github.com/rmcelreath/rethinking#installation)
Stan
* [`cmdstanr`](https://mc-stan.org/cmdstanr/articles/cmdstanr.html)
* [`RStan`](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started)
* [`brms`](r/brms/#how-do-i-install-brms)
Targets
* [`targets`](https://github.com/ropensci/targets/#installation)
* [`stantargets`](https://github.com/ropensci/stantargets/#installation)
V8, needed for the `dagitty` package
* [`V8`](https://github.com/jeroen/v8#installation)
## Project structure
This repository is structured with a `homework/` folder for homework solutions,
and `notes/` folder for notes. For folks joining in the colearning group,
you are encouraged to make your own branch in this repository and
share your notes and/or homework solutions.
The `R/` folder can be used to store reusable functions useful across
homework solutions and your own model situations.
For example, the `dag_plot` function makes a DAG plot from a DAG:
```{r readme_dag, cache = TRUE}
library(ggplot2)
library(ggdag)
library(dagitty)
source('R/dag_plot.R')
dag <- dagify(
Z ~ A + B,
B ~ A,
exposure = 'A',
outcome = 'Z'
)
dag_plot(dag)
```
## Branches
See the full list of [branches](https://github.com/robitalec/statistical-rethinking-colearning-2022/branches).
* [Matteo](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/matteo)
* [Jillian](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/jillian)
* [Alec](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/alec)
* [Levi](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/levi)
* [Katrien](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/katrien)
* [Bella](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/bella)
* [Hannah](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/hannah)
* [Frankie](https://github.com/robitalec/statistical-rethinking-colearning-2022/tree/frankie)
## Thanks
Many thanks to Richard McElreath for a continued emphasis on teaching
Bayesian statistics and for providing this incredible resource of lectures
and homework assignments free for everyone.
Also thank you to the developers of R, Stan and innumerous R packages that
allow us to pursue this interest.
## Code of Conduct
Please note that this project is released with a [Code of
Conduct](CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.