Model-Averaged Propensity Scores Selected by Prognostic-Score Balance // Constructs a model-averaged propensity score as a convex combination of candidate propensity score models, with mixing weights selected on a simplex grid to optimize covariate or prognostic-score balance, implementing the method of Kabata, Stuart and Shintani (2024) doi:10.1186/s12874-024-02350-y. Prognostic scores follow Hansen (2008) doi:10.1093/biomet/asn004: outcome models are fit on untreated units only. The resulting score is designed to be supplied directly to the matchit() function of 'MatchIt' as a distance measure or to the weightit() function of 'WeightIt' as a propensity score, with balance assessment via 'cobalt'.