avlm

• vcov() on an avlm object returns the covariance used for inference (classical, HC or CR1).
• optimal_g() gains a nu argument, so it can tune g for cluster-robust inference (optimal_g(G, nu = G - 1, alpha = 0.05)).
• The summary prints the value of g.

Bug Fixes

• The heteroskedasticity-robust (vcov_estimator) F-test now uses the Wald statistic of the tested coefficients, inverting their covariance rather than taking a block of the full precision matrix, and stores it as Q / d. Previously the evidence was overstated when testing more than one coefficient.
• confint() no longer misplaces intervals when the fit has aliased (NA) coefficients; previously every coefficient after an aliased one got NA or the wrong standard error. Heteroskedasticity- and cluster-robust summaries of such fits no longer fail with a singular-matrix error.
• Heteroskedasticity- and cluster-robust standard errors now account for weights (rows and residuals are scaled by sqrt(w); zero weights are dropped). Previously the weights were ignored, giving wrong standard errors for weighted fits.
• Intercept-only models now work with vcov_estimator and cluster.
• cluster now accepts one observation per cluster, which gives the HC0 sandwich scaled by n / (n - 1).
• optimal_g() now searches below g = 1, where the optimum lies for small samples.
• Anytime-valid ANOVA p-values use the number of observations as the sample size; models without an intercept were off by one.
• av() checks that g is a single positive number and gives informative errors for glm, multivariate (mlm) and multi-stratum (Error()) fits instead of failing later or returning meaningless output.

Internal

• The classical, heteroskedasticity-robust and cluster-robust code paths share one covariance routine, and unused helpers were removed.