Being at Princeton, I’m surrounded by computational modeling, so I will aggregate sources here.

**Network analysis**

- Node centrality
- List of connectivity measures
- Centrality & power
**Zhang 2017:**Degree centrality, betweenness, and closeness in social networks- Which centrality measure should I use?
- Network centrality
- social network analysis with R
- Exploring correlations in R with corrr
- How to create correlation networks with ggraph
- announcing ggraph

**Neural network**

**Reinforcement Learning**

- Tutorial: Link

**Dimensionality Reduction**

- Theory
- PCA
- MDS
- Uniform Manifold and Projection (Umap)
- Cran umap package PDF
- umapr package
- Tutorial
- Dimensionality reduction with UMap
- I was under the impression that UMAP was generally better at preserving global structure than tSNE, but this paper [1] suggests it’s the worst. Anyone have insights or intuition?
- Performance comparisons of dimensionality reduction techniques
- Using umap in R with python
- Intuitive explanation of how UMap works compared to T-SNE

- T-sne
- Youtube tutorial
- van der Maaten 2008: Visualizing Data using t-SNE

**Machine Learning**

**Drift Diffusion**

- Drift Diffusion model is a computational model of decision making that decomposes reaction times and responses into cognitive components. The linked package is a specific python package I have used before.
- Heirarchical Bayesian method: Link
- hBayesDM: Link

**Social Modeling**

-J