Location-Varying Gating for Mixtures of Quantile Regressions // Extends finite mixtures of quantile regressions (the 'mixqr' package) with a concomitant-covariate, quantile-indexed mixing gate. The mixing probabilities follow a multinomial-logit model whose coefficients may depend on a separate set of gating covariates and on the quantile level, so that latent-class membership can change across the conditional distribution. This turns the location-varying mixing idea of Furno (2025) into a likelihood and EM object with proper standard errors on the gate. Estimation reuses the 'mixqr' component and error-density machinery; the gate is fit by a weighted multinomial-logistic M-step and reduces exactly to a constant gate when the gating model is an intercept only.