Initial CRAN release.
localAteDS() aggregate function computes the local
ATE estimate and, optionally, the variance statistics (arm-specific
residual sums of squares and within-server projected scatter) needed for
the delta-method variance. Set compute_variance = FALSE to
limit disclosure to the three scalars strictly needed for the point
estimate (n_k, n_k_1, tau_k). The
heterogeneous argument (default TRUE)
additionally returns covariate column sums, allowing the client to
assemble the global covariate mean for the leverage-corrected variance
estimator.
addStudyIdDS() assign function appends K-1 integer
study-dummy columns to a server-side data frame, supporting the adjusted
generalised difference estimator when study membership directly shifts
the outcome.
All functions enforce DataSHIELD disclosure controls: privacy
control level is checked via
dsBase::checkPermissivePrivacyControlLevel() and minimum
subset size is validated against nfilter.subset from
dsBase::listDisclosureSettingsDS().