Package: nftbart 2.1

Rodney Sparapani

nftbart: Nonparametric Failure Time Bayesian Additive Regression Trees

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 <doi:10.1111/biom.13857>.

Authors:Rodney Sparapani [aut, cre], Robert McCulloch [aut], Matthew Pratola [ctb], Hugh Chipman [ctb]

nftbart_2.1.tar.gz
nftbart_2.1.tar.gz(r-4.5-noble)nftbart_2.1.tar.gz(r-4.4-noble)
nftbart_2.1.tgz(r-4.4-emscripten)nftbart_2.1.tgz(r-4.3-emscripten)
nftbart.pdf |nftbart.html✨
nftbart/json (API)
NEWS

# Install 'nftbart' in R:
install.packages('nftbart', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • CDCheight - CDC height for age growth charts
  • bmx - NHANES 1999-2000 Body Measures and Demographics
  • lung - NCCTG Lung Cancer Data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cppopenmp

1.15 score 14 scripts 205 downloads 8 exports 5 dependencies

Last updated 1 years agofrom:9327361c87. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 22 2024
R-4.5-linux-x86_64NOTEDec 22 2024

Exports:bMMCDimputeCindexnftnft2tsvstsvs2xicuts

Dependencies:latticeMatrixnnetRcppsurvival