Changes in version 1.0.2 (2026-09-26)                  

  - Resubmission to CRAN. Fixed the reviewer notes from Konstanze
    Lauseker: removed \examples from the unexported internal functions
    new_xplus() and validate_xplus(); the package no longer uses
    \dontrun{} anywhere; and xplus() no longer reads or writes
    .GlobalEnv directly (the optional seed argument uses a plain
    set.seed() call).

                        Changes in version 1.0.1                        

  - Resubmission to CRAN. Fixed NOTEs reported in the incoming pre-test:
    reworded Description to avoid false-positive spelling flags and
    removed the private development repository URL from NEWS.md and
    README.md.

                        Changes in version 1.0.0                        

This is a major, breaking upgrade of the PLUS-derived package
extensions, not an exact reproduction of the paper or evidence of
predictive superiority. Response-scale model scores are not guaranteed
to be calibrated probabilities. The package remains xplus; the
production repository is https://github.com/alrobles/xplus. Development,
audit harnesses and historical baselines live in a separate development
repository.

Breaking interfaces and metrics

  - Require R >= 4.1.0 for native-pipe syntax and declare glmnet
    >= 4.1-8. Verification uses R 4.5.2 / glmnet 5.0; other supported
    versions have not thereby been verified.
  - Reject learning_rate = 0; the accepted interval is (0, 1].
  - Interpret binary factors by labels, not level codes. Single-lambda
    class predictions now have fixed factor levels "0", "1"; multiple
    lambdas retain a 0/1 matrix. Classification uses a strict >
    object$cutoff comparison.
  - Accept positional newx in predict(). Validate finite numeric
    features; named training features require an exact set of nonempty
    unique prediction names and are automatically aligned. Reject
    mismatches and unsupported dots.
  - Respect s for training and new-data prediction, including numeric
    lambdas. Cache-only objects support only "lambda.min" without new
    data. get_auc() forwards prediction arguments and requires one
    lambda; assess() can return per-lambda metrics.
  - Require finite binary truth, or finite nonnegative two-column
    negative/positive masses for soft truth or counts
    (assess()/auc_matrix()). Soft vectors are not binary truth. Weights
    must be finite, nonnegative and row-aligned, without recycling.
    Invalid rows are errors, not silently dropped observations.
  - Undefined AUC returns NA with a warning when either effective class
    mass is zero. Zero effective total mass/weight makes assessment
    undefined. Ties receive half credit; constant scores with both
    classes have AUC 0.5.
  - MSE and MAE retain the two-class-column loss convention: twice
    scalar binary loss. Class-mass totals contribute to effective row
    weights. Deviance clips probabilities to [1e-5, 1 - 1e-5]; other
    metrics do not inherit that clipping.

Sampling, CV and stopping

  - Default sampling = "bootstrap" preserves replacement multiplicities
    as case weights on unique rows. Duplicate identities never cross CV
    folds. sampling = "unique" retains legacy deduplication, not all
    legacy behavior.
  - sample_use_time is a budget of completed inclusion rounds per
    identity, not bootstrap draws. Record both sampling_counts and
    draw_counts.
  - Default cv_measure = "deviance" in both iterative and final fitting
    stages. AUC remains opt-in, with an explicit warning and deviance
    fallback for fewer than 10 observations per fold on average;
    iterative effective measures appear in history.
  - Require at least three positive and three unlabeled input
    observations. Reduce requested folds to available class counts;
    require three unique members of each iterative training class and
    retry degenerate samples at most 20 times before reporting
    stop_reason = "degenerate_labels".
  - Preserve the selected global-update, hard-thresholded iterative
    training path when learning_rate < 1; learning_rate = 1 uses sampled
    Bernoulli updates. Final targets remain soft in both paths.
    Reference sampling budgets and smoothing should not be mistaken for
    newly invented package extensions.
  - Expose sigmoid_scale = 10 separately from glmnet elastic-net alpha.
  - Default min_iter = 5, stability_window = 5, min_coverage = 0.9.
    Stability compares undamped mapped scores against previous
    pseudo-labels over all unlabeled cases. A full window of consecutive
    scores strictly above convergence_threshold and minimum coverage are
    required; damping alone cannot create convergence. Record max_iter,
    budget_exhausted, degenerate_labels and label_stability distinctly
    rather than implying every stop is success.

Final fitting and reproducibility

  - Validate effective class mass on the full final dataset and every CV
    training split (mean positive proportion strictly within 1e-5 and 1
    - 1e-5). Soft labels all above 0.5 remain valid when mass is
    sufficient; hard-class counts do not trigger final fallback.
  - Warn when reverting to original PU labels and record fallback_used
    and fallback_reason. Preserve original_y, proposed pseudo_labels,
    actual final_labels/y, iteration history, sampling/draw counts and
    final_foldid. Insufficient original-label mass is an error, not a
    hidden recovery.
  - Isolate an explicit seed from the caller's RNG stream and restore
    its prior state, including when no .Random.seed existed.

Documentation and verification

  - Regenerate reference manuals from current roxygen contracts.
    Installation examples target alrobles/xplus without embedded
    credentials.
  - Diagnostic harnesses, the historical numerical-audit baseline and
    candidate diagnostics are maintained in a separate development
    repository; they are not part of the production source tree.
  - Document Rscript -e 'devtools::test(stop_on_failure = TRUE)' and
    require inspection of FAIL/WARN/SKIP counts. Keep build/check output
    outside the tracked checkout.

                        Changes in version 0.1.1                        

  - Fixed a crash when the pseudo-labels of the iterative training
    subset collapse to a single class: fitting now stops cleanly with
    stop_reason = "degenerate_labels" instead of failing inside
    glmnet::cv.glmnet().
  - Sampling probabilities are now clamped at zero when the
    unlabeled-sampling budget is decremented, and sampling is skipped
    once the budget is fully exhausted (avoids an uninformative sample()
    error).

                        Changes in version 0.1.0                        

  - Initial release.
  - Implements the PLUS algorithm for positive-unlabeled learning (Zhou
    et al., 2022, doi:10.1371/journal.pcbi.1009956).
  - Provides xplus() fitting function with iterative pseudo-label
    updates.
  - S3 methods: predict(), coef(), summary(), print(), assess(),
    get_auc(), and get_predictions().
  - Ships bundled datasets: lacs, lacsSample, and binexample.