Nathaniel E. Helwig develops statistical methods at the intersection of psychology and computational statistics. His University of Minnesota research biography emphasizes multivariate and nonparametric statistics for complex observations.[ref]
His Generalized Linear Models in R by Nathaniel E. Helwig explains how a link function connects a response's conditional expected value with a linear predictor. For logistic regression, the predictor represents log odds; for Poisson regression, a logarithmic link ensures a positive fitted mean. This separates the model for the mean from the response's probability distribution, while making deviance and dispersion part of model assessment.[ref]
Generalized Linear Models in R by Nathaniel E. Helwig. Section 1 and logistic/Poisson examples; the notes explicitly credit their textbook basis
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