Inference of nonparametric tests as linear models I found some statements throughout the web that suggest that most common statistical tests can be performed as general(ized) linear models (cf.  here). For a Wilcoxon test the author of the referenced source exemplarily suggests to first transform the original data into signed ranks and then to run a linear model. Imo this procedure won't give reliable p values because now the residuals are of course not normal but uniform. However, I wonder whether a generalized linear model could help here (e.g. after transforming the ranks to a range of [0,1] and then apply a GLM with beta link)? If so, which GLM could be applied here? And any idea how this analysis could be performed using R? (The Wilcoxon test was just an example - how could this look like for a Spearman correlation analysis?)

You might be interested in the proportional odds ordinal logistic regression (POOLR) model.
Wilcoxon-Mann-Whitney U is a score test of a POOLR model with one variable indicating group membership, similar to how an equal-variance t-test is a Wald test of a linear regression with one variable indicating group membership.
This is not the same as applying a regression model to the rank-transformed data, but that seems like a hack to use until one learns about the POOLR model.
