Skip to content

Semi-Supervised Classification Methods

0packages depend
3.3Kdownloads / year
91.3%test coverage
13/13flavors OK

Overview

About

Provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Install

Health

CRAN checks
13OK
Slowest check: 2.5 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-04
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 2.1-0
READMEYes · 185 wordsVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
24
Examples that run
82%9 run in a plain check · 2 never run
Exported functions with examples
73%
Documented parameters
99%
Return-value docs
100%
Help pages citing references
25%

Also includes 11 demo scripts (run with demo())

Downloads

3.3K
CRAN downloads in the past year
Rank #12,462 · ~9/day · ~272/mo
Daily download trend is not available in this view yet.
27430 days
90190 days
3.3K1 year
Compare downloads with other packages →
Also on77 r2u9 autocran541 conda_forge

Repository

9Stars
5Forks
3Open issues
0Open PRs
0Releases
136Commits
5Commit identities
136 commits · Last activity 2019-12-16 · 0% stars, 30d
Responsiveness
How these are sampled
PRs merged (all time)
100%
Median days to close, 50 most recently updated closed issues
not measured
Median days to close, 50 most recently updated closed PRs
1

Stars over time

2023-09-14 · 92026-07-07 · 9
PracticesBeta

2 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/mabelc/ssc on 2026-10-21.

Docs source (1)
README.Rmd
Lint, format, editor (1)
RStudio project
How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
9 external dependencies (excludes base and recommended)

Installing ssc adds 1 package (1 with compiled code, 70 KB of source tarballs). Heaviest: proxy 70 KB.

Depends (1)
R >= 3.2.3
Imports (2)
statsproxy
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (6)
Thesis advisor, Maintainer since 1.0 (2016)
Author · added in 2.0.0
Contributor
Thesis advisor
Listed in earlier versions (1)
Osmani Rosado
no longer listed · 1.0
Releases (3)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • 2.1-0Latest
    record
    2019-12-15 · current release · diff ↗
  • R
    R 3.6.0 released · 2019-04-26
  • R
    R 3.5.0 released · 2018-04-23
  • 2.0.0
    record
    2018-03-27 · diff ↗ ·
    removed 3 exports
    bClassif, bClassifOneNN, statistics
    added 8 exports
    coBCCombine, coBCG, democraticCombine, democraticG, selfTrainingG, setredG, triTrainingCombine, triTrainingG
  • R
    R 3.4.0 released · 2017-04-21
  • 1.0
    record
    2016-10-05
  • R
    R 3.3.0 released · 2016-05-03

Package metadata

First published
2016-10-05
Total releases
3 / 10 yrs
License
GPL (>= 3) OSI
Minimum R
≥ 3.2.3
Bundled data
125 KB / 2 files
Download size
437 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("ssc")
Bergmeir, C., Benítez, J. M., González, M., Rodríguez, J. D., Rosado-Falcón, O., & Triguero, I. (2019). ssc: Semi-Supervised Classification Methods (Version 2.1-0) [Computer software]. https://doi.org/10.32614/CRAN.package.ssc

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for ssc version 2.1-0 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

Report a problem with this page →

Privacy choices

These apply to this browser and are stored on this device only. Nothing about your choice is sent to us.

Read the privacy policy