Methodology

- Interesting Twitter thread on [common mistakes in meta-analyses](https://twitter.com/rrarroca/status/1786186734633414841), and [guidelines and resources to carry out meta-analyses](https://twitter.com/Ed_pheasant/status/1789268392358060535), and a [nice visualization](https://x.com/MatthewBJane/status/1808278216831861033/photo/1) of random-effects meta-analysis.
- [Course on Bayesian inference with Nimble](https://jabiologo.github.io/web/tutorials/IBER24.html).
- An example of [paper](https://onlinelibrary.wiley.com/doi/10.1111/geb.13858) following PRISMA guidelines and the revtools package.
- An introduction to the [bootstrap](Elements of Statisical Learning- Data Mining, Inference and Prediction), with interactive plots.
- A [list of R books: The big book of R](https://www.bigbookofr.com/).
- An ["Everything is fucked" syllabus](https://thehardestscience.com/2016/08/11/everything-is-fucked-the-syllabus/) on the flaws of many common methods 
- A [reflection](https://thehardestscience.com/2018/11/02/data-analysis-is-thinking-data-analysis-is-theorizing/) on how data analysis and theoretical thinking are intertwined.

Causality, strutural equation models, path analysis

- A [comparison](https://rpubs.com/jebyrnes/brms_bayes_sem) of implementations of SEM in lavaan, piecewiseSEM and brms.