Changes in version 0.1.0 (2026-11-13)                  

Initial CRAN release.

Features

  - 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 \eqn{\sigma_1^2} and \eqn{\sigma_0^2} rather than a
    pooled \eqn{\sigma^2}, 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.