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Found 8 papers, 2 papers with code
Spatio-seasonal risk assessment of upward lightning at tall objects using meteorological reanalysis data
The model performs best in winter, with the highest predicted UL risk coinciding with observed peaks in measured lightning at tall objects.
Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees
Growth curve models are popular tools for studying the development of a response variable within subjects over time.
Upward lightning at wind turbines: Risk assessment from larger-scale meteorology
This leads to a large underestimation of the proportion of LLS-non-detectable UL at wind turbines, which is the dominant lightning type in the cold season.
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
A related approach, called "model-based forests", that is geared towards randomized trials and simultaneously captures effects of both prognostic and predictive variables, was introduced by Seibold, Zeileis and Hothorn (2018) along with a modular implementation in the R package model4you.
Hybrid Machine Learning Forecasts for the UEFA EURO 2020
Based on the resulting estimates, the tournament is simulated repeatedly and winning probabilities are obtained for all teams.
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
At the core of the package are algorithms for highly-efficient Bayesian estimation and inference that can be applied to generalized additive models (GAMs) or generalized additive models for location, scale, and shape (GAMLSS), also known as distributional regression.
Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019
Finally, based on the resulting estimates, the FIFA Women's World Cup 2019 is simulated repeatedly and winning probabilities are obtained for all teams.
Transformation Forests
A more general understanding of regression models as models for conditional distributions allows much broader inference from such models, for example the computation of prediction intervals.
