Initial CRAN release.
ds.EstimateAte() orchestrates federated ATE
estimation across K independent studies using arm-specific OLS models
fitted via ds.glm().
compute_variance argument (default
TRUE) returns a delta-method variance estimate and 95%
confidence interval assembled from per-server residual sums of squares
and projected scatter scalars, with no additional communication round.
Set to FALSE to limit server disclosure to the three
scalars strictly needed for the point estimate (n_k,
n_k_1, tau_k).
heteroscedastic_arms argument (default
FALSE) estimates arm-specific noise variances and rather
than a pooled , adjusting the variance formula in both the delta-method
and homogeneous closed-form branches.
heterogeneous argument (default TRUE)
uses the leverage-corrected delta-method variance (valid under covariate
shift and heterogeneous treatment probabilities across sites). Set to
FALSE for the simpler homogeneous closed form.
study_effects argument (default FALSE)
enables the adjusted generalised difference estimator for settings where
study membership directly shifts the outcome. Appends K-1 integer
study-dummy columns server-side via addStudyIdDS() and
adjusts degrees of freedom accordingly.
Input validation helpers .check_datasources(),
.check_args(), and .check_string() give
informative errors on bad arguments.
.check_sample_sizes() warns when either federated
arm size falls below 5*(d+1), indicating the asymptotic approximation
may be unreliable.