An Ensemble Method for Interval-Censored Survival Data

Implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) .


The goal of ICcforest is to implement the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit.

Installation

You can install the released version of ICcforest from CRAN with:

install.packages("ICcforest")

Example

This is a basic example which shows you how to solve a common problem:

library(ICcforest)
library(survival)
library(icenReg)
#> Loading required package: Rcpp
#> Loading required package: coda
data(miceData)
 
## For ICcforest to run, Inf should be set to be a large number, for example, 9999999.
idx_inf <- (miceData$u == Inf)
miceData$u[idx_inf] <- 9999999.
 
## Fit an iterval-censored conditional inference forest
Cforest <- ICcforest(Surv(l, u, type = "interval2") ~ grp, data = miceData)
#> mtry = 1  OOB Brier score = 0.06497173 
#> Searching left ...
#> Searching right ...

News

ICcforest 0.5.0

  • Added a NEWS.md file to track changes to the package.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("ICcforest")

0.5.0 by Weichi Yao, 2 months ago


Browse source code at https://github.com/cran/ICcforest


Authors: Weichi Yao [aut, cre] , Halina Frydman [aut] , Jeffrey S. Simonoff [aut]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports stats, utils, graphics, survival, icenReg, ipred, parallel

Depends on partykit

Suggests LTRCtrees, inum, bayesSurv


See at CRAN