High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). 
              Currently supports frailty data analysis. Allows for Weibull and 
              Exponential distribution. Includes function for interval censored data.

Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.

Implementation of methodology designed to perform: (i) variable 
    selection, (ii) effect estimation, and (iii) uncertainty quantification, 
    for high-dimensional survival data. Our method uses a spike-and-slab prior 
    with Laplace slab and Dirac spike and approximates the corresponding 
    posterior using variational inference, a popular method in machine learning 
    for scalable conditional inference. Although approximate, the variational 
    posterior provides excellent point estimates and good control of the false 
    discovery rate. For more information see Komodromos et al. (2021) 
    <arXiv:2112.10270>.

Functions can be used for batch survival analysis, 
	but not only for it. Most importantly, it can verify any
 	P-value calculated according to the gene expression level and 
	eliminate the influence of co-expression genes.

A high-level interface to perform survival analysis, 
    including Kaplan-Meier analysis and log-rank tests and Cox regression.
    Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting.
    Builds upon the 'survminer' package for Kaplan-Meier plots and provides
    a customizable implementation for forest plots.
    Kaplan & Meier (1958) <doi:10.1080/01621459.1958.10501452>
    Cox (1972) <JSTOR:2985181>
    Peto & Peto (1972) <JSTOR:2344317>.

An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in <https://github.com/luyouepiusf/SurvivalClusteringTree>.