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ssel

Semi-Supervised Ensemble Learning

ssel provides three related method layers for tabular regression:

weighted ensembles over caret-supported learners, with cross-validated tuning, out-of-fold diagnostics, signed residual-offset estimates, and two explicit response/dataset selection policies;
iterative multi-response input-space expansion with Jacobi or asymmetric Gauss–Seidel companion updates, package-defined fixed or shadow gates, and per-response iteration stitching;
package-defined pseudo-label promotion based on a cross-model and cross-dataset range ratio, with an out-of-fold squared-correlation gauge for accepting or reverting candidate rounds.

The default supervised learners are random forest (ranger), gradient boosting (gbm), and multivariate adaptive regression splines (earth). Any compatible caret method can be supplied through the documented method arguments.

Installation

ssel requires R >= 4.1.0.

# Released version
install.packages("ssel")

The library and the ssel command-line interface are installed together from the repository root with the product installer, documented in install/USAGE.md. The R package alone, without the command, installs from a checkout with:

R CMD INSTALL lib

Open the RStudio project inside lib/ for package development.

Start here

The quickstart builds a minimal in-memory dataset and introduces the public entry points:

vignette("ssel-quickstart", package = "ssel")

The method articles define the mathematics, package policies, API mapping, and limitations of each helper family:

vignette("ensemble-theory",  package = "ssel")
vignette("chain-regression", package = "ssel")
vignette("semi-supervised",  package = "ssel")

Pipeline entry points compose the lower-level helpers and write their results to caller-supplied directories. Their exact input schemas, side effects, and failure conditions are documented in the function reference:

?modelPipeline
?chainPipeline
?semiSupervisedPipeline

Rendered documentation: https://kanameishi.github.io/ssel/.

Public API

Family	Functions	Articles
Pipeline entry points	`modelPipeline()`, `chainPipeline()`, `semiSupervisedPipeline()`, `postProcess()`	Ensemble estimators and residual offsets; Iterative multi-response input expansion; Range-ratio pseudo-label promotion
Dataset assembly	`buildDataset()`	Getting started with ssel
Training and prediction	`trainModel()`, `trainRegressionModel()`, `predictModel()`, `aggregateResponses()`	Ensemble estimators and residual offsets
OOF reconstruction and diagnostics	`oofEnsemble()`, `auditQuantiles()`, `auditOverfit()`, `detectOutliers()`	Ensemble estimators and residual offsets
Companion gates	`extractChainImportance()`, `activeByImportance()`, `activeByShadow()`, `computeActiveByImportance()`	Iterative multi-response input expansion
Utilities	`toNumeric()`, `which.nonnum()`, `removeOutliersIQR()`	Getting started with ssel
Installation diagnosis	`describeModule()`	ssel command-line interface

Full reference: help(package = "ssel") or https://kanameishi.github.io/ssel/reference/.

Citation

@Manual{Verri_ssel,
  title  = {{ssel}: Semi-Supervised Ensemble Learning},
  author = {Verri, Alejandro Daniel},
  year   = {2026},
  url    = {https://kanameishi.github.io/ssel/}
}

License and contact

AGPL version 3 or later; see LICENSE. The maintainer is Alejandro Verri Kozlowski (ORCID 0000-0002-8535-1170), Universidad de Buenos Aires, Facultad de Ingenieria. Contact information is listed in DESCRIPTION.

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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