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LongituRF  

Random Forests for Longitudinal Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("LongituRF", "0.9")



Attach the package and use:
library("LongituRF")
Maintained by
Louis Capitaine
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-08-31
Latest Update: 2020-08-31
Description:
Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) Random forests for high-dimensional longitudinal data.
How to cite:
Louis Capitaine (2020). LongituRF: Random Forests for Longitudinal Data. R package version 0.9, https://cran.r-project.org/web/packages/LongituRF
Previous versions and publish date:
No previous versions
Other packages that cited LongituRF R package
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Other R packages that LongituRF depends, imports, suggests or enhances
Functions, R codes and Examples using the LongituRF R package
Some associated functions: DataLongGenerator . MERF . MERT . Moy . Moy_exp . Moy_fbm . Moy_sto . REEMforest . REEMtree . bay.exp . bay.fbm . bay . bay_sto . cov.exp . cov.fbm . gam_exp . gam_fbm . gam_sto . logV.exp . logV.fbm . logV . opti.FBM . opti.FBMreem . opti.exp . predict.exp . predict.fbm . predict.longituRF . predict.sto . sig.exp . sig.fbm . sig . sig_sto . sto_analysis . 
Some associated R codes: LongituRF.R .  Full LongituRF package functions and examples
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