2 papers
cs.LG2026
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
Martin Jullum, Lars Henry Berge Olsen, Jon Lachmann +1
This paper introduces the shapr R package, a versatile tool for generating Shapley value-based prediction explanations for machine learning and statistical regression models. Moreo…
stat.ME2025
FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
Florian Frommlet, Jon Lachmann, Geir Storvik +1
The FBMS R package facilitates Bayesian model selection and model averaging in complex regression settings by employing a variety of Monte Carlo model exploration methods. At its c…