{ "_id": "6a3d8993a319ef941447c504", "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\",\nrole = c(\"aut\", \"cre\", \"cph\"))", "Description": "An extensible expectation-maximization (EM) framework for\nfinite mixtures of quantile regressions (clusterwise /\nmixture-of-experts quantile regression). A single EM substrate\nwith an engine/extension contract carries a family of\ncapabilities: the core free-weight mixture of Wu and Yao (2016)\n -- a fast asymmetric-Laplace\npath and the nonparametric kernel-density EM with components\nconstrained to have their tau-quantile equal to zero (Hall and\nPresnell 1999 device); expectile and M-quantile component-loss\nfamilies (Newey and Powell 1987; Breckling and Chambers 1988);\ncomponent-specific penalized variable selection (SCAD /\nadaptive-LASSO, the quantile analogue of Khalili and Chen\n2007); and joint multi-quantile estimation with a shared latent\nclassification and non-crossing component curves. Provides\nclassification-aware standard errors (sparsity and\nstochastic-EM multiple imputation), multi-start estimation,\ncomponent-count selection, and prediction. 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