Tutorials

Practical guides that I publish on Towards Data Science and Medium. Every guide is written as a Quarto document, so the data and code to reproduce its figures are public in the TDS-Medium_Blog repository.

Bayesian regression with brms

• Beyond the Mean: Embracing Uncertainty in Measurements with Bayesian Analysis and Multilevel Modeling. Multilevel models for repeated measurements. Code
• Do not over-think about “outliers”, use a Student-t distribution instead. A robust alternative when extreme observations are part of your data. Code
• Avoiding abuse and misuse of T-test and ANOVA: Regression for categorical responses. Ordinal regression with the cumulative family. Code
• Improving the analysis of object (or cell) counts with lots of zeros. Hurdle and zero-inflated models. Code

Spatial analysis of cells

• Introduction to spatial analysis of cells for neuroscientists (part 1). Point pattern analysis with spatstat. Code
• Where a cell sits matters more than how many there are (part 2). Raster layers and point process models for how neurons, astrocytes and microglia sit relative to each other. Code

These guides are the practical companion of my research papers on point pattern and topological data analysis. The repository also holds further guides in R and Python (topological data analysis, Bayesian reanalysis of open data) that are still being prepared for publication.

Writing on science and publishing

• We Reward the Paper, Not the Truth: What Publish-or-Perish Teaches Young Scientists
• What does the Dana-Farber misconduct scandal reveal about scientific integrity in the XXI century?

Learn the foundations first

New to R, project setup or data management? Start with the free open course materials.