Extensible Finite Mixtures of Quantile and Expectile Regressions // An extensible expectation-maximization (EM) framework for finite mixtures of quantile regressions (clusterwise / mixture- of-experts quantile regression). A single EM substrate with an engine/extension contract carries a family of capabilities: the core free-weight mixture of Wu and Yao (2016) doi:10.1016/j.csda.2014.04.014 -- a fast asymmetric-Laplace path and the nonparametric kernel-density EM with components constrained to have their tau-quantile equal to zero (Hall and Presnell 1999 device); expectile and M-quantile component-loss families (Newey and Powell 1987; Breckling and Chambers 1988); component-specific penalized variable selection (SCAD / adaptive-LASSO, the quantile analogue of Khalili and Chen 2007); and joint multi-quantile estimation with a shared latent classification and non-crossing component curves. Provides classification-aware standard errors (sparsity and stochastic- EM multiple imputation), multi-start estimation, component- count selection, and prediction. The companion package 'mixqrgate' adds location-varying gating.