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Sstack  

Bootstrap Stacking of Random Forest Models for Heterogeneous Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("Sstack", "1.0.1")



Attach the package and use:
library("Sstack")
Maintained by
Kevin Matlock
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-05-01
Latest Update: 2018-05-01
Description:
Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.
How to cite:
Kevin Matlock (2018). Sstack: Bootstrap Stacking of Random Forest Models for Heterogeneous Data. R package version 1.0.1, https://cran.r-project.org/web/packages/Sstack. Accessed 01 Feb. 2025.
Previous versions and publish date:
No previous versions
Other packages that cited Sstack R package
View Sstack citation profile
Other R packages that Sstack depends, imports, suggests or enhances
Complete documentation for Sstack
Functions, R codes and Examples using the Sstack R package
Some associated functions: BSHorizontalStack . BSVerticalStack . BSstack . BSstack_predict . StackData . 
Some associated R codes: BSHorizontalStack.R . BSVerticalStack.R . BSstack.R . BSstack_predict.R .  Full Sstack package functions and examples
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