Package: ssel 0.4.0

ssel: Semi-Supervised Ensemble Learning

Weighted-ensemble regression over base learners supported by 'caret' (Kuhn (2008) <doi:10.18637/jss.v028.i05>), with cross-validated hyperparameter selection, out-of-fold diagnostics, and signed residual- offset estimates. Multi-response problems use iterative input-space expansion related to Spyromitros-Xioufis et al. (2016) <doi:10.1007/s10994-016-5546-z>, with Jacobi or 'Gauss-Seidel' sweeps, package-defined companion gates and per-response iteration stitching. A package-defined pseudo-label stage promotes prediction rows by a cross-model and cross-dataset range ratio and accepts rounds with an out-of-fold squared-correlation gauge.

Authors:Alejandro Verri Kozlowski [aut, cre, cph]

ssel_0.4.0.tar.gz
ssel_0.4.0.zip(r-4.7-any)ssel_0.4.0.zip(r-4.6-any)ssel_0.4.0.zip(r-4.5-any)
ssel_0.4.0.tgz(r-4.6-any)ssel_0.4.0.tgz(r-4.5-any)
ssel_0.4.0.tar.gz(r-4.7-any)ssel_0.4.0.tar.gz(r-4.6-any)
ssel_0.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ssel/json (API)

# Install 'ssel' in R:
install.packages('ssel', repos = c('https://averrik.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/averrik/ssel/issues

Pkgdown/docs site:https://averrik.github.io

On CRAN:

Conda:

3.60 score 293 downloads 20 exports 84 dependencies

Last updated from:608eaa5286. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK242
source / vignettesOK311
linux-releaseOK198
macos-releaseOK193
macos-oldrelOK134
windows-develOK142
windows-releaseOK146
windows-oldrelOK136
wasm-releaseOK145

Exports:activeByImportanceactiveByShadowaggregateResponsesauditOverfitauditQuantilesbuildDatasetchainPipelinecomputeActiveByImportancedetectOutliersextractChainImportancemodelPipelineoofEnsemblepostProcesspredictModelremoveOutliersIQRsemiSupervisedPipelinetoNumerictrainModeltrainRegressionModelwhich.nonnum

Dependencies:caretclasscliclockcodetoolscpp11crayondata.tabledbscandiagramdigestdoParalleldplyre1071earthfarverforeachFormulafuturefuture.applygbmgenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnumDerivparallellypillarpkgconfigplotmoplotrixplyrpROCprodlimprogressrproxypurrrR6rangerRColorBrewerRcppRcppEigenrecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Ensemble estimators and residual offsets
Choose the right API layer | Notation and cells | Fitting and OOF scores | Unseen-row ensembles are cell-local | Independent OOF residual reconstruction | Signed residual offsets | Two distinct selection rules | Optimism diagnostic | Executable residual-offset example | References
Rendered fromensemble-theory.Rmdusingknitr::rmarkdown

Last update: 2026-08-24
Started: 2026-07-28

Iterative multi-response input expansion
Responses, row classes, and companion values | Jacobi recurrence | Asymmetric Gauss--Seidel recurrence | Companion-importance normalization | Fixed and shadow gates | Active-set history | Change score and stopping policy | Per-response iteration stitch | Interpretation and limits | References
Rendered fromchain-regression.Rmdusingknitr::rmarkdown

Last update: 2026-08-24
Started: 2026-07-28

Range-ratio pseudo-label promotion
Symbols and input pools | Optional augmented-data warm start | Range-ratio promotion score | Key budget and pseudo-label rows | OOF acceptance gauge | State transitions and return value | Final re-emission boundary | Limitations | References
Rendered fromsemi-supervised.Rmdusingknitr::rmarkdown

Last update: 2026-08-24
Started: 2026-07-28

Getting started with ssel
Three questions, three helpers | Minimum data contract | Diagnose and normalize numeric text | Create the three model-input products | When to use each helper | Important limits and side effects
Rendered fromssel-quickstart.Rmdusingknitr::rmarkdown

Last update: 2026-07-28
Started: 2026-07-28