mixqr: Extensible Finite Mixtures of Quantile and Expectile Regressions
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.
| Version: |
0.2.0 |
| Depends: |
R (≥ 4.1) |
| Imports: |
quantreg, stats, graphics, utils |
| Suggests: |
ggplot2, rqPen, testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2026-06-25 |
| DOI: |
10.32614/CRAN.package.mixqr |
| Author: |
Kailas Venkitasubramanian [aut, cre, cph] |
| Maintainer: |
Kailas Venkitasubramanian <kailasv at gmail.com> |
| BugReports: |
https://github.com/kvenkita/mixqr/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/kvenkita/mixqr,
https://kvenkita.github.io/mixqr/ |
| NeedsCompilation: |
no |
| Citation: |
mixqr citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
mixqr results |
Documentation:
Downloads:
Reverse dependencies:
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