The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
av.lm() gains a cluster argument for CR1
cluster-robust standard errors (the sandwich scaled by
G / (G - 1), without Stata/fixest’s extra
(n - 1) / (n - k)). When cluster is supplied,
the anytime-valid p-values, F-test, and confidence intervals use
G - 1 (clusters minus one) as the denominator degrees of
freedom, rather than n - p (observations minus
coefficients). cluster cannot be combined with
vcov_estimator. The method is described in Lindon (2026),
“Anytime-Valid Inference in Linear Models with Clustered Outcomes: Exact
Multivariate Tests and Cluster-Robust Confidence Sequences”, https://arxiv.org/abs/2610.25613.cluster accepts a vector of cluster identifiers, a
one-sided formula (cluster = ~ cyl), or a column name
(cluster = "cyl"). Formulas and column names are looked up
in the model’s data and aligned with the observations used
in the fit.cluster, the anytime-valid sample size is the
number of clusters G, so g is on the cluster
scale, and the joint F-test uses the cluster-robust e-value with
exponent G/2. The F-test p-value is 1 when the
cluster-robust covariance of the tested coefficients is singular
(e.g. fewer clusters than tested coefficients).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)).g.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.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.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.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.summary.lm() and
summary.aov() with sequential p-values and confidence
intervals.av(): applies the anytime-valid transformation to
lm and aov objects.confint(): computes anytime-valid confidence intervals
for regression coefficients.roxygen2.testthat.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.