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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.0 |
rolling linux/jammy R-4.5 | mixqr_0.2.0.tar.gz |
450.9 KiB |
0.2.0 |
rolling linux/noble R-4.5 | mixqr_0.2.0.tar.gz |
451.4 KiB |
0.2.0 |
rolling source/ R- | mixqr_0.2.0.tar.gz |
332.3 KiB |
0.2.0 |
latest linux/jammy R-4.5 | mixqr_0.2.0.tar.gz |
450.9 KiB |
0.2.0 |
latest linux/noble R-4.5 | mixqr_0.2.0.tar.gz |
451.4 KiB |
0.2.0 |
latest source/ R- | mixqr_0.2.0.tar.gz |
332.3 KiB |
0.2.0 |
2026-04-23 source/ R- | mixqr_0.2.0.tar.gz |
0 B |