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mixqr  

Extensible Finite Mixtures of Quantile and Expectile Regressions
View on CRAN: Click here


Download and install mixqr package within the R console
Install from CRAN:
install.packages("mixqr")

Install from Github:
library("remotes")
install_github("cran/mixqr")

Install by package version:
library("remotes")
install_version("mixqr", "0.2.0")



Attach the package and use:
library("mixqr")
Maintained by
Kailas Venkitasubramanian
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-25
Latest Update: 2026-06-25
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) <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.
How to cite:
Kailas Venkitasubramanian (2026). mixqr: Extensible Finite Mixtures of Quantile and Expectile Regressions. R package version 0.2.0, https://cran.r-project.org/web/packages/mixqr. Accessed 26 Oct. 2026.
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