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xplus 1.0.2

• 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).

xplus 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.

xplus 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 alrobles/xplus on GitHub. Development, audit harnesses and historical baselines live in the private development repository alrobles/xplus-develeopment.

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 the private development repository alrobles/xplus-develeopment; 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.

xplus 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).

xplus 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.