{"id":32200112,"url":"https://github.com/enriquegit/ssr","last_synced_at":"2026-07-12T22:31:10.089Z","repository":{"id":56936788,"uuid":"202154442","full_name":"enriquegit/ssr","owner":"enriquegit","description":"An R package with Semi-Supervised Regression Methods","archived":false,"fork":false,"pushed_at":"2019-09-05T12:14:20.000Z","size":189,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-04-18T07:11:33.050Z","etag":null,"topics":["data-science","machine-learning","r","regression","semi-supervised-learning"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/enriquegit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-08-13T13:48:54.000Z","updated_at":"2023-04-20T08:22:01.000Z","dependencies_parsed_at":"2022-08-21T05:50:52.485Z","dependency_job_id":null,"html_url":"https://github.com/enriquegit/ssr","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/enriquegit/ssr","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enriquegit%2Fssr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enriquegit%2Fssr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enriquegit%2Fssr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enriquegit%2Fssr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/enriquegit","download_url":"https://codeload.github.com/enriquegit/ssr/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/enriquegit%2Fssr/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35404819,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-12T02:00:06.386Z","response_time":87,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","machine-learning","r","regression","semi-supervised-learning"],"created_at":"2025-10-22T03:39:25.104Z","updated_at":"2026-07-12T22:31:10.082Z","avatar_url":"https://github.com/enriquegit.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ssr \u003cimg src=\"man/figures/ssrlogo.png\" align=\"right\" width=\"100px \" alt=\"\"/\u003e\n\n\u003c!-- badges: start --\u003e\n[![CRAN\\_Status\\_Badge](http://www.r-pkg.org/badges/version/ssr)](https://cran.r-project.org/package=ssr)\n[![Travis build status](https://travis-ci.org/enriquegit/ssr.svg?branch=master)](https://travis-ci.org/enriquegit/ssr)\n[![](http://cranlogs.r-pkg.org/badges/grand-total/ssr?color=blue)](https://cran.r-project.org/package=ssr)\n\u003c!-- badges: end --\u003e\n\n\nAn R package for **semi-supervised regression**.\n\nThe **ssr** package implements *Co-training by Committee* and *self-learning* semi-supervised learning (SSL) algorithms for **regression**. In semi-supervised learning, algorithms learn model's parameters not only from labeled data but also from unlabeled data. In many applications, it is difficult, expensive, time-consuming, etc. to label data. Thus, semi-supervised methods learn by combining the limited labeled data points and the unlabeled data points.\n\nThe **ssr** package provides the following functionalities:\n\n* Train Co-training by Committee models.\n* Train self-learning models.\n* Track and plot performance during training.\n* Generate plots to quickly visualize the results.\n* User can specify the base regressors to be used by the Co-training committee and self-learning from the [caret](https://github.com/topepo/caret) package, other packages or custom functions.\n\n## Installation\n\nYou can install the **ssr** package from CRAN:\n\n```{r}\ninstall.packages(\"ssr\")\n```\n\nor you can install the development version from GitHub.\n\n```{r}\n# install.packages(\"devtools\")\ndevtools::install_github(\"enriquegit/ssr\")\n```\n\n## Example\n\nThe following example shows how to train a Co-training Committee of two regressors: a linear model and a KNN.\n\n```{r}\nlibrary(ssr)\n\ndataset \u003c- friedman1 # Load friedman1 dataset.\n\nset.seed(1234)\n\n# Prepare de data\nsplit1 \u003c- split_train_test(dataset, pctTrain = 70)\nsplit2 \u003c- split_train_test(split1$trainset, pctTrain = 5)\nL \u003c- split2$trainset\nU \u003c- split2$testset[, -11] # Remove the labels.\ntestset \u003c- split1$testset\n\n# Define list of regressors.\nregressors \u003c- list(linearRegression=lm, knn=caret::knnreg)\n\n# Fit the model.\nmodel \u003c- ssr(\"Ytrue ~ .\", L, U, regressors = regressors, testdata = testset)\n\n# Plot RMSE.\nplot(model)\n\n# Get the predictions on the testset.\npredictions \u003c- predict(model, testset)\n\n# Calculate RMSE on the test set.\nsqrt(mean((predictions - testset$Ytrue)^2))\n\n```\n\n*For detailed explanations and more examples refer to the package* [vignettes](https://CRAN.R-project.org/package=ssr/vignettes/ssr-package-vignette.html).\n\n## Citation\n\nTo cite package **ssr** in publications use:\n\n```{r}\nEnrique Garcia-Ceja (2019). ssr: Semi-Supervised Regression Methods.\nR package https://CRAN.R-project.org/package=ssr\n```\n\nBibTex entry for LaTeX:\n\n```{r}\n@Manual{enriqueSSR,\n    title = {ssr: Semi-Supervised Regression Methods},\n    author = {Enrique Garcia-Ceja},\n    year = {2019},\n    note = {R package},\n    url = {https://CRAN.R-project.org/package=ssr},\n  }\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenriquegit%2Fssr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fenriquegit%2Fssr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fenriquegit%2Fssr/lists"}