Changes in version 1.0.1 (2026-07-10)                  

  - Learner engines now run single-threaded by default (ranger
    num.threads = 1, xgboost nthread = 1), in line with CRAN's
    at-most-2-cores policy; raise via control for real analyses (fixes
    the CRAN incoming pre-test NOTE "Re-building vignettes had CPU
    time 7.7 times elapsed time"). Selection results are unchanged.

                        Changes in version 1.0.0                        

Initial release, implementing Kabata, Stuart & Shintani (2024), BMC
Medical Research Methodology 24:228, doi:10.1186/s12874-024-02350-y.

  - psave(): model-averaged propensity scores as a convex combination of
    candidate models ("glm", "rpart", "ranger", "xgboost" by default;
    any "SL.*" SuperLearner wrapper; or user-supplied
    ps.matrix/prog.matrix), with mixing weights selected on a simplex
    grid.
  - ps.append / prog.append: extra user-supplied candidate score columns
    (a vector of length n, or a matrix/all-numeric data frame with
    unique column names) appended AFTER the candidates from
    ps.methods/ps.matrix and prog.methods/prog.matrix. Appended
    propensity columns are validated (strictly in (0, 1)) and clipped
    like every other candidate; grid tie-breaking favors the base
    candidates. Supplying prog.matrix or prog.append without outcome is
    an explicit error (prognostic candidates require the outcome; gamma
    is selected by outcome-prediction MSE among untreated units).
  - Four selection criteria from the paper: "prog" (weighted ASMD of the
    model-averaged prognostic score, the recommended default;
    per-candidate targets via prog.target), "smd", "ks", and "logloss".
    Estimands: ATT (default) and ATE, with the supplement's
    estimand-specific weight formulas.
  - Prognostic scores per Hansen (2008): candidate outcome models fit on
    untreated units only; mixing weights gamma selected by unweighted
    untreated-set MSE. gaussian() and binomial() outcome families.
  - average = FALSE vertex mode selects the single best candidate
    propensity score by the chosen criterion.
  - Ecosystem integration: fit$ps drops into MatchIt::matchit(distance =
    ) and WeightIt::weightit(ps = ); psave_match() / psave_weight()
    wrappers reuse the stored formula and data to eliminate
    row-misalignment; cobalt::bal.tab() works directly on psave objects.
  - Full S3 suite: print() (with the literal next call), summary()
    (mixing weights, all-criteria diagnostics table, full balance
    table), plot() ("balance", "distribution", "criterion"), fitted(),
    weights(), predict() (with keep.fits = TRUE).
  - Exported utilities: simplex_grid() (integer-composition simplex
    enumeration defining the tie-breaking order) and psave_criteria()
    (all four criteria for any propensity score vector).
  - Documented fixes relative to the paper's reference code:
    integer-arithmetic simplex grid (the reference's floating-point
    rowSums == 1 filter silently dropped ~10.6% of grid points); proper
    weighted-eCDF KS statistic; binomial() family for binary responses;
    no train/test-inconsistent scale(); strict complete-case handling
    (NAs error, never dropped). See vignette("method-details", package =
    "psAve").
  - Three vignettes: Getting Started (matching workflow), Reproducing
    the published IPW workflow (ATT/ATE weighting with
    survey::svyglm()), and Method details.