Recent Posts

A few lessons learned along the way in completing my PhD

The mysteries multiply as we look through the literature but some answers are still forthcoming.

The mystery continues, leading us down an algorithmic path filled with broken sticks.

A parameter collapses, a sophisticated model looms afoot and we find the start of a trail of clues in this unexpected mystery.

An overview of two of the main obstacles to causal inference: Confounding and Selection Bias

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Recent & Upcoming Talks

The presence of specific urban environment resources such as food vendors and recreation centers may impact diet and physical activity, …

Causal Inference is the foundation of most scientific inquiry. In this simple case study we walk through several key concepts involved …

Recent Publications

Built environment features (BEFs) refer to aspects of the human constructed environment, which may in turn support or restrict health …

The rstap package implements Bayesian spatial temporal aggregated predictor models in R using the probabilistic programming language …