Overview
Mixtures of life distributions provide a convenient framework for survival analysis: particularly when standard models such as the Weibull or the log-normal are unable to capture some features from the data. These mixtures can also account for unobserved heterogeneity or outlying observations.
BASSLINE (BAyeSian Survival anaLysIs usiNg shapE mixtures of log-normal distributions) uses shape mixtures of log-normal distributions to fit data with fat tails and has been adapted from code written by Vallejos & Steel[1]. Some of the functions have been rewritten in C++ for increased performance.
5 distributions from the log-normal family are supported by BASSLINE:
- The log-normal distribution
- The log student’s T distribution
- The log-logistic distribution
- The log-Laplace distribution
- The log-exponential power distribution
As well as MCMC (Markov chain Monte Carlo) algorithms for the 5 distributions, additional functions which allow log-marginal likelihood estimators and deviance information criteria to be calculated are provided. Case deletion analysis and outlier detection are also supported.