variance = "louis" computes the
gate-block covariance by Louis's (1982) identity -- complete-data information
minus the missing information from the latent labels -- giving analytic,
classification-aware standard errors without the imputation cost of "stochEM".
In a Monte-Carlo check (n = 300) the Louis SE matched the empirical SD (0.207
vs 0.211) and reached 0.95 coverage, where the conditional sandwich gave 0.80.
This is the variance DESIGN section 2.4 specified; it is now the recommended
choice for gate inference. summary() labels the SE method in use.Revision after two adversarial reviews. The estimator was verified correct; this release fixes the inference honesty and the software contract.
mixqrgate(variance = "stochEM") adds a
multiple-imputation gate variance that propagates the uncertainty about class
membership. The default sandwich SE is conditional on the fitted memberships
and under-covers when components overlap; summary() now says so.sim_gate2() gains loc_vary, which makes
membership genuinely depend on the quantile rank. The vignette is rewritten
around inference (does the gate vary, with uncertainty shown) rather than an
eyeballed trend; the previous example overstated a noisy drift.y, X, and Z stay aligned.confint() returns gate-coefficient intervals (previously empty); AIC(k=)
honors k..Rbuildignore, Remotes: for the non-CRAN mixqr dependency.Still deferred (documented): full Louis joint gate+component information, a
cross-tau Wald test, KDE-path classification-aware gate SEs, cross-tau relabel
coherence, and vary_gating = "smooth".
First release. A companion to mixqr adding a concomitant, quantile-indexed mixing gate to finite mixtures of quantile regressions.
mixqrgate() fits a gated mixture: the mixing probabilities follow a
multinomial-logit gate on concomitant covariates (gating = ~ z), optionally
refit per quantile level (vary_gating = "discrete", the Furno 2025
location-varying mixing mode). ALD and Wu & Yao kernel-density paths.mixqr fit) when
gating = ~1.mixqr component and constrained-error-density machinery via its
extension API (weighted_rq(), constrained_kde()).print, summary (component + gate blocks), coef, vcov,
predict, plot(which = "gating") (the gate-vs-tau picture), logLik,
AIC, BIC, fitted, residuals.sim_gate2() simulates a two-component design with a concomitant gate.