Nonparametric Failure Time Bayesian Additive Regression Trees
Deprecated: use bMM instead
Create a matrix out of a vector or data.frame
Cold-deck missing imputation
Calculate the C-index/concordance for survival analysis.
Fit NFT BART models.
Estimating the survival and the hazard for AFT BART models.
Drawing Posterior Predictive Realizations for NFT BART models.
Variable selection with NFT BART models.
Specifying cut-points for the covariates
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>.