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nftbart  

Nonparametric Failure Time Bayesian Additive Regression Trees
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("nftbart", "2.1")



Attach the package and use:
library("nftbart")
Maintained by
Rodney Sparapani
[Scholar Profile | Author Map]
First Published: 2021-12-20
Latest Update: 2023-11-28
Description:
Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a complete description of the model at .
How to cite:
Rodney Sparapani (2021). nftbart: Nonparametric Failure Time Bayesian Additive Regression Trees. R package version 2.1, https://cran.r-project.org/web/packages/nftbart. Accessed 04 Apr. 2025.
Previous versions and publish date:
1.1 (2021-12-20 10:20), 1.2 (2022-02-03 20:40), 1.3 (2022-03-29 21:30), 1.4 (2022-08-26 00:20), 1.5 (2023-01-06 22:50), 1.6 (2023-05-01 00:50)
Other packages that cited nftbart R package
View nftbart citation profile
Other R packages that nftbart depends, imports, suggests or enhances
Complete documentation for nftbart
Functions, R codes and Examples using the nftbart R package
Some associated functions: CDCheight . CDimpute . Cindex . bMM . bartModelMatrix . bmx . lung . nft2 . predict.aftree . predict.nft2 . tsvs2 . xicuts . 
Some associated R codes: CDimpute.R . Cindex.R . bMM.R . bartModelMatrix.R . concordance.R . nft.R . nft2.R . predict.aftree.R . predict.nft.R . predict.nft2.R . tsvs.R . tsvs2.R . xicuts.R .  Full nftbart package functions and examples
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