Being at Princeton, I’m surrounded by computational modeling, so I will aggregate sources here.
- 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
- Tutorial: Link
- Uniform Manifold and Projection (Umap)
- Cran umap package PDF
- umapr package
- Dimensionality reduction with UMap
- I was under the impression that UMAP was generally better at preserving global structure than tSNE, but this paper  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
- 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