Package: ssr 0.1.1
Enrique Garcia-Ceja
ssr: Semi-Supervised Regression Methods
An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions.
Authors:
ssr_0.1.1.tar.gz
ssr_0.1.1.zip(r-4.5)ssr_0.1.1.zip(r-4.4)ssr_0.1.1.zip(r-4.3)
ssr_0.1.1.tgz(r-4.4-any)ssr_0.1.1.tgz(r-4.3-any)
ssr_0.1.1.tar.gz(r-4.5-noble)ssr_0.1.1.tar.gz(r-4.4-noble)
ssr_0.1.1.tgz(r-4.4-emscripten)ssr_0.1.1.tgz(r-4.3-emscripten)
ssr.pdf |ssr.html✨
ssr/json (API)
NEWS
| # Install 'ssr' in R: |
| install.packages('ssr', repos = c('https://enriquegit.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/enriquegit/ssr/issues
- friedman1 - Friedman1 dataset.
data-sciencemachine-learningregressionsemi-supervised-learning
Last updated 5 years agofrom:bbf935ecac. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 08 2024 |
| R-4.5-win | NOTE | Nov 08 2024 |
| R-4.5-linux | NOTE | Nov 08 2024 |
| R-4.4-win | NOTE | Nov 08 2024 |
| R-4.4-mac | NOTE | Nov 08 2024 |
| R-4.3-win | NOTE | Nov 08 2024 |
| R-4.3-mac | NOTE | Nov 08 2024 |
Exports:split_train_testssr
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| friedman1 dataset. | friedman1 |
| Plots a ssr object | plot.ssr |
| Predictions from a fitted ssr object | predict.ssr |
| Splits a data frame into train and test sets. | split_train_test |
| Fits a semi-supervised regression model | ssr |