Frontiers

Toward interpretable expected goals modeling using Bayesian mixed models

Empowered by technological progress, sports teams and bookmakers strive to understand relationships between player and team activity and match outcomes. For this purpose, the probability of an event ...
GitHub

R vs. Python for Data Science

Hello! This Web page is aimed at shedding some light on the perennial R-vs.-Python debates in the Data Science community. This is largely (though not exclusively) a debate between the Statistics (R) ...
GitHub

Tigramite – Causal inference for time series datasets

Update: Tigramite now has a new CausalEffects class that allows to estimate (conditional) causal effects and mediation based on assuming a causal graph. Have a look at the tutorial. Further, Tigramite ...
Frontiers

Linear Mixed-Effect Models Through the Lens of Hardy–Weinberg Disequilibrium

For genetic association studies with related individuals, the linear mixed-effect model is the most commonly used method. In this report, we show that contrary to the popular belief, this standard ...
Wilson Center

Lieutenant General H.R. McMaster

H. R. McMaster was the 26th assistant to the president for National Security Affairs. He served as a commissioned officer in the United States Army for thirty-four years before retiring as a ...
Nature

Mixed linear model approach adapted for genome-wide association studies

Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally ...