mixqr

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.

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Checks run against github.com/kvenkita/mixqr on 2026-07-19.

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First published

2026-06-25

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1 / 1 yrs

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MIT + file LICENSE OSI

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