Notebooks
Please find here any jupyter notebooks that I may have thought would be of general interest. For now, these are all about PyMC, a Python library for doing Bayesian analysis.
PyMC
There are many valuable tools for fitting hierarchical models in ecology, including unmarked, JAGS, NIMBLE and Stan. These are, for the most part, R libraries or programs called from R. There are relatively fewer examples of how to fit these models in Python. While most ecologists use R, they may find some benefit from using Python. For example, despite ecology being a lucrative industry, some of us might have to pivot to another field where Python may be more common. Also, Python is widely used for machine learning, which is increasingly applied in ecology.
In the PyMC jupyter notebooks, I try to demonstrate how to use PyMC to train the most common hierarchical models in ecology. For this, I have drawn considerable inspiration from Royle and Dorazio (2008), Kéry and Schaub (2011), McCrea and Morgan (2014), and Hooten and Hefley (2019), oftentimes simply porting their code, ideas, and analyses. In doing so, I hope to demonstrate PyMC’s core features, and highlight its strengths and weakenesses. The PyMC notebooks are somewhat sequential, with earlier notebooks explaining more basic features.