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ssel 0.3.1

Bug fixes

• trainRegressionModel() now restores the exact named R2 and RMSE score lists for each response–dataset cell before unseen-row prediction. Ensemble weights and method pruning are therefore cell-local and no longer inherit state from the last OOF cell visited. Weight formulas, equal-weight fallbacks, output schemas, and selectors are unchanged.

Documentation

• The ensemble, iterative multi-response, and semi-supervised articles now define their symbols, equations, API boundaries, package policies, and limitations directly from the implemented public contracts.
• Retired design and unsupported terminology were removed, including claims of predictive quantiles, calibrated uncertainty, Boruta, scale-free convergence, tri-training, held-out validation, and guaranteed predictive gain.

ssel 0.3.0

CRAN normalization

• Progress, diagnostics, warnings, and fatal failures now use R condition channels instead of raw console output. Quiet mode suppresses progress and structural messages without hiding warnings or errors.
• Package-created parallel backends are limited to two workers and are reset on exit, including failure paths.
• Prediction contract failures no longer enter an interactive debugger.
• auditOverfit() now discovers mixed-case caret method names consistently with oofEnsemble().
• Source builds exclude internal dev/ and TITO/ material. DESCRIPTION and README metadata now match the implemented public API and include verified method references.

Semi-supervised pipeline

• New exported stage: semiSupervisedPipeline(). Package-defined range-ratio pseudo-label promotion with an out-of-fold squared-correlation gauge over original labelled rows, a key budget, absolute-tolerance reversion with an accepted-state refit, and final serial cell-file re-emission. With .path.iter pointing to a chainPipeline() iteration directory, the baseline can use one selected augmented dataset snapshot.

ssel 0.2.0

Bug fixes

• trainRegressionModel() now promotes integer predictor columns to numeric after reading train/test split CSVs in the training, metrics, and prediction paths. This keeps caret model frames type-stable when data.table::fread() infers integer in one split and double in another, avoiding prediction-time class mismatch failures without changing the numeric values.

Development notes

• The semi-supervised prototype remains quarantined under inst/dev/sot/ and is not part of the public API in this release. The redesign items are tracked as planned work before any exported semi-supervised entry point is added.

ssel 0.1.2

Bug fixes

• buildDataset(), modelPipeline(), and chainPipeline() now accept an optional exact features vector for base predictors. When supplied, train/test splits use only those predictor columns plus chain-generated prior-iteration columns governed by ITER_SUFFIX, and fail clearly if a requested predictor is missing or if a response/identifier column is listed as a predictor. The default features = NULL preserves legacy predictor inference.

ssel 0.1.1

Bug fixes

• chainPipeline(resume = TRUE) now fails before attempting to read metrics when the resume state is incomplete. If .path.iter/convergence.csv and .path.iter/Y.csv exist but .path.summary/metrics.csv is missing, the error names the missing artifact and tells the caller to start a fresh run with resume = FALSE or restore the summary metrics artifact.
• trainRegressionModel() now skips response/dataset cells whose training response has no variance before calling caret::train(). The warning names the affected cell and reports the row count, unique response count, minimum, and maximum, avoiding low-level errors such as invalid number of intervals for unsupported response/dataset intersections.
• chainPipeline() now snapshots and stitches all summary artifacts consistently in the final per-response best-iteration pass. metrics.csv, response_long.csv, residuals_oof.csv, prediction_quantiles.csv, and overfit.csv are all taken from the same selected iteration per response, avoiding reports that mixed stitched metrics with last-iteration residuals.

ssel 0.1.0

Initial release.

Exported pipeline entry points

• dataPipeline() — assembles per-dataset CSVs from the manifest-declared domain CSVs.
• modelPipeline() — single-response weighted-ensemble fit with 5-fold cross-validation, OOF residuals, signed residual-offset summaries, and an in-sample-vs-CV optimism audit.
• chainPipeline() — multi-response iterative refinement with two sweep orders ("jacobi" and "gauss-seidel"), package-defined active-set gates, a positional change heuristic, and per-response iteration stitching.

Exported helpers

Dataset assembly, training, prediction, residual audit, and chain gating helpers: buildDatasets, buildDataset, trainModel, trainRegressionModel, predictModel, aggregateResponses, oofEnsemble, auditQuantiles, auditOverfit, detectOutliers, extractChainImportance, activeByImportance, activeByShadow, computeActiveByImportance, removeOutliersIQR, toNumeric, which.nonnum.

Documentation

• vignette("ssel-quickstart", package = "ssel") — minimal usage template.
• vignette("ensemble-theory", package = "ssel") — single-response estimators: weighted-ensemble construction, OOF residual reconstruction, signed offsets, optimism diagnostic, and the two response/dataset selectors.
• vignette("chain-regression", package = "ssel") — multi-response input expansion: row-class-specific Jacobi and asymmetric Gauss–Seidel updates, package-defined fixed and shadow gates, stopping policy, and per-response iteration stitch.
• The initial release also shipped a non-production semi-supervised design. Version 0.3.0 replaced it with the implemented range-ratio promotion policy.

The bibliography for all four vignettes lives in inst/REFERENCES.bib.

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.
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