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>.

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.

Tools for downloading and extracting data from the Copernicus "Agrometeorological indicators 
  from 1979 to present derived from reanalysis"
  <https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview> (AgERA5).

Calculate 'ActiGraph' counts from the X, Y, and Z axes of a triaxial 
    accelerometer. This work was inspired by Neishabouri et al. who published the 
    article "Quantification of Acceleration as Activity Counts in 'ActiGraph' Wearables" 
    on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) 
    and 'python' implementation of this code (<https://github.com/actigraph/agcounts>).

Tools for the analysis of growth data: to extract an 
    LMS table from a gamlss object, to calculate the standard 
    deviation scores and its inverse, and to superpose two wormplots 
    from different models. The package contains a some varieties of 
    reference tables, especially for The Netherlands.