Package: mixqr Type: Package Title: Extensible Finite Mixtures of Quantile and Expectile Regressions Version: 0.2.0 Authors@R: person("Kailas", "Venkitasubramanian", email = "kailasv@gmail.com", role = c("aut", "cre", "cph")) Description: 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) -- 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. License: MIT + file LICENSE Encoding: UTF-8 LazyData: true Depends: R (>= 4.1) Imports: quantreg, stats, graphics, utils Suggests: ggplot2, rqPen, testthat (>= 3.0.0), knitr, rmarkdown RoxygenNote: 7.3.3 Config/testthat/edition: 3 Config/Needs/website: pkgdown VignetteBuilder: knitr URL: https://github.com/kvenkita/mixqr, https://kvenkita.github.io/mixqr/ BugReports: https://github.com/kvenkita/mixqr/issues NeedsCompilation: no Packaged: 2026-06-25 19:59:29 UTC; root Author: Kailas Venkitasubramanian [aut, cre, cph] Maintainer: Kailas Venkitasubramanian Repository: https://cran.r-universe.dev Date/Publication: 2026-06-25 18:29:07 UTC RemoteUrl: https://github.com/cran/mixqr RemoteRef: HEAD RemoteSha: d9c629744b446cd9fd7717c6f140cc713180cba5