Extensible Finite Mixtures of Quantile and Expectile Regressions R package mixqr version 0.2.0 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. Software Depends: R (>= 4.1) Imports: quantreg, stats, graphics, utils Suggests: ggplot2, rqPen, testthat (>= 3.0.0), knitr, rmarkdown Kailas Venkitasubramanian <kailasv@gmail.com> Comprehensive R Archive Network (CRAN) Kailas Venkitasubramanian [aut, cre, cph] MIT + file LICENSE (https://CRAN.R-project.org/package=mixqr/LICENSE) 2026-06-25 application/tgz https://CRAN.R-project.org/package=mixqr doi:10.32614/CRAN.package.mixqr