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ssr  

Semi-Supervised Regression Methods
View on CRAN: Click here


Download and install ssr package within the R console
Install from CRAN:
install.packages("ssr")

Install from Github:
library("remotes")
install_github("cran/ssr")

Install by package version:
library("remotes")
install_version("ssr", "0.1.1")



Attach the package and use:
library("ssr")
Maintained by
Enrique Garcia-Ceja
[Scholar Profile | Author Map]
First Published: 2019-08-20
Latest Update: 2019-09-02
Description:
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.
How to cite:
Enrique Garcia-Ceja (2019). ssr: Semi-Supervised Regression Methods. R package version 0.1.1, https://cran.r-project.org/web/packages/ssr. Accessed 20 Apr. 2025.
Previous versions and publish date:
0.1.0 (2019-08-20 11:30)
Other packages that cited ssr R package
View ssr citation profile
Other R packages that ssr depends, imports, suggests or enhances
Complete documentation for ssr
Functions, R codes and Examples using the ssr R package
Some associated functions: friedman1 . plot.ssr . predict.ssr . split_train_test . ssr . 
Some associated R codes: algorithms.R . data.R . utility_functions.R .  Full ssr package functions and examples
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