Package: nftbart
Type: Package
Title: Nonparametric Failure Time Bayesian Additive Regression Trees
Version: 2.3
Date: 2025-12-02
Authors@R: c(person('Rodney', 'Sparapani', 
	   role=c('aut', 'cre'), email='rsparapa@mcw.edu'),
	   person('Robert', 'McCulloch', role='aut'),
  	   person('Matthew', 'Pratola', role='ctb'), 
	   person('Hugh', 'Chipman', role='ctb'))
Author: Rodney Sparapani [aut, cre], Robert McCulloch [aut], Matthew
        Pratola [ctb], Hugh Chipman [ctb]
Maintainer: Rodney Sparapani <rsparapa@mcw.edu>
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 description of the
        model at <doi:10.1111/biom.13857>.
License: GPL (>= 2)
Depends: R (>= 4.2.0), survival, nnet, lattice
Imports: Rcpp
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2026-07-04 18:53:22 UTC; root
Repository: https://rsparapa.r-universe.dev
Date/Publication: 2025-12-03 08:47:08 UTC
RemoteUrl: https://github.com/cran/nftbart
RemoteRef: HEAD
RemoteSha: 2ca4ed21be01d7d3a350fd0f99c5fd77feb5b09e