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>.

Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox>.

Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models with left, right or interval censored survival data 
      using maximum penalised likelihood. A 'non-parametric' smooth estimate of the baseline hazard
      function is provided.

Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods.

Several functions and S3 methods to predict survival by using neural networks. We implemented Partial Logistic Artificial Neural Networks (PLANN) as proposed by Biganzoli et al. (1998)  <https://pubmed.ncbi.nlm.nih.gov/9618776>.

Provides estimates for the bivariate and trivariate distribution 
             functions and bivariate and trivariate survival functions for 
             censored gap times. Two approaches, using existing methodologies, 
             are considered: (i) the Lin's estimator, which is based on the 
             extension the Kaplan-Meier estimator of the distribution function 
             for the first event time and the Inverse Probability of Censoring 
             Weights for the second time (Lin DY, Sun W, Ying Z (1999) 
             <doi:10.1093/biomet/86.1.59> and (ii) another estimator
             based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008)
             <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). 
             The proposed methods are the landmark estimators based on 
             subsampling approach, and the estimator based on weighted cumulative
             hazard estimator. The package also provides nonparametric estimator
             conditional to a given continuous covariate. All these methods have
             been submitted to be published.