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RFmstate  

Random Forest-Based Multistate Survival Analysis
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("RFmstate", "0.1.2")



Attach the package and use:
library("RFmstate")
Maintained by
Yiqing Chen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-11
Latest Update: 2026-03-11
Description:
Fits cause-specific random survival forests for flexible multistate survival analysis with covariate-adjusted transition probabilities computed via product-integral. State transitions are modeled by random forests. Subject-specific transition probability matrices are assembled from predicted cumulative hazards using the product-integral formula. Also provides a standalone Aalen-Johansen nonparametric estimator as a covariate-free baseline. Supports arbitrary state spaces with any number of states (three or more) and any set of allowed transitions, applicable to clinical trials, disease progression, reliability engineering, and other domains where subjects move among discrete states over time. Provides per-transition feature importance, bias-variance diagnostics, and comprehensive visualizations. Handles right censoring and competing transitions. Methods are described in Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> for random survival forests, Putter et al. (2007) <doi:10.1002/sim.2712> for multistate competing risks decomposition, and Aalen and Johansen (1978) <https://www.jstor.org/stable/4615704> for the nonparametric estimator.
How to cite:
Yiqing Chen (2026). RFmstate: Random Forest-Based Multistate Survival Analysis. R package version 0.1.2, https://cran.r-project.org/web/packages/RFmstate. Accessed 13 Sep. 2026.
Previous versions and publish date:
(2026-09-09 20:30), 0.1.2 (2026-03-11 17:40)
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Complete documentation for RFmstate
Functions, R codes and Examples using the RFmstate R package
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