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literanger  

Random Forests for Multiple Imputation Based on 'ranger'
View on CRAN: Click here


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

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

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



Attach the package and use:
library("literanger")
Maintained by
Stephen Wade
[Scholar Profile | Author Map]
First Published: 2023-06-26
Latest Update: 2023-07-13
Description:
An updated implementation of R package 'ranger' by Wright et al, (2017) for training and predicting from random forests, particularly suited to high-dimensional data, and for embedding in 'Multiple Imputation by Chained Equations' (MICE) by van Buuren (2007) . Ensembles of classification and regression trees are currently supported. Sparse data of class 'dgCMatrix' (R package 'Matrix') can be directly analyzed. Conventional bagged predictions are available alongside an efficient prediction for MICE via the algorithm proposed by Doove et al (2014) . Survival and probability forests are not supported in the update, nor is data of class 'gwaa.data' (R package 'GenABEL'); use the original 'ranger' package for these analyses.
How to cite:
Stephen Wade (2023). literanger: Random Forests for Multiple Imputation Based on 'ranger'. R package version 0.1.1, https://cran.r-project.org/web/packages/literanger. Accessed 11 May. 2025.
Previous versions and publish date:
0.0.1 (2023-06-26 14:20), 0.0.2 (2023-07-13 23:30), 0.1.0 (2024-09-04 01:20)
Other packages that cited literanger R package
View literanger citation profile
Other R packages that literanger depends, imports, suggests or enhances
Complete documentation for literanger
Functions, R codes and Examples using the literanger R package
Some associated functions: literanger-package . predict.literanger . train . 
Some associated R codes: cpp11.R . literanger-package.R . predict.R . train.R . utility.R .  Full literanger package functions and examples
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