Provides pathwise estimation of regularized logistic propensity
score models using covariate balancing loss functions rather than maximum
likelihood. Regularization paths are fit via the 'adelie' elastic-net
solver with a 'glmnet'-like interface, yielding balancing weights that target
covariate balance for the ATE and ATT. Under lasso penalization, lambda
bounds the maximum covariate imbalance, so the regularization path traces a
sequence of decreasing imbalance tolerances. For details, see Sverdrup &
Hastie (2026)