Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package.
            AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016)
             <doi:10.21307/stattrans-2016-046>.

Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) <doi:10.1080/03610918.2016.1177072>.

Implements several basic algorithms for estimating regression parameters for semiparametric accelerated failure time (AFT) model. The main methods are: Jin rank-based method (Jin (2003) <doi:10.1093/biomet/90.2.341>), Heller’s estimating method (Heller (2012) <doi:10.1198/016214506000001257>), Polynomial smoothed Gehan function method (Chung (2013) <doi:10.1007/s11222-012-9333-9>), Buckley-James method (Buckley (1979) <doi:10.2307/2335161>) and Jin`s improved least squares method (Jin (2006) <doi:10.1093/biomet/93.1.147>). This package can be used for modeling right-censored data and for comparing different estimation algorithms.

A collection of model checking methods for semiparametric 
    accelerated failure time (AFT) models under the rank-based approach. For the 
    (computational) efficiency, Gehan's weight is used. It provides functions to 
    verify whether the observed data fit the specific model assumptions such as 
    a functional form of each covariate, a link function, and an omnibus test. 
    The p-value offered in this package is based on the Kolmogorov-type supremum 
    test and the variance of the proposed test statistics is estimated through 
    the re-sampling method. Furthermore, a graphical technique to compare the 
    shape of the observed residual to a number of the approximated realizations 
    is provided. See the following references; A general model-checking 
    procedure for semiparametric accelerated failure time models, Statistics and 
    Computing, 34 (3), 117 <doi:10.1007/s11222-024-10431-7>; Diagnostics for 
    semiparametric accelerated failure time models with R package 'afttest', 
    arXiv, <doi:10.48550/arXiv.2511.09823>.