avlm 0.2.0
New Features
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.- With
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)).- 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.
avlm 0.1.0
New Features
- First release of the avlm package, which implements
anytime-valid inference for linear models and ANOVA.
- Provides a drop-in replacement for
summary.lm() and
summary.aov() with sequential p-values and confidence
intervals.
- Includes support for:
av(): applies the anytime-valid transformation to
lm and aov objects.confint(): computes anytime-valid confidence intervals
for regression coefficients.
Infrastructure
- Full documentation with
roxygen2.
- Unit tests using
testthat.
Notes
- Based on the methodology described in
“Anytime-Valid Linear Models and Regression Adjusted Causal
Inference in Randomized Experiments”
available at: https://arxiv.org/abs/2210.08589