The program performes the Chi-Square and Kolmogorov-Smirnov tests to verify if a given sample is from a hypothesized parametric distribution including exponential, normal, lognormal, Weibull and Poisson distributions.

The program demonstrates nonparameteric estimation procedures of life data.

The program demonstrates a sample size estimation procedure used in nonparameteric reliability analysis.

The program demonstrates the Maximum Likelihood and Probability Paper methods of parameter estimation for some popular distributions including exponential, normal, lognormal and Weibull.

The program demonstrates classical estimation of the exponential distribution based on type I and II life test data with and without replacement.

The program demonstrates interval estimation for the binomial distribution parameter, unknown CDF at a fixed exposure, as well as normal and lognormal distribution parameters.

The program demonstrates the Bayesian estimation procedures for binomial and Poisson distributions using conjugate and non-conjugate prior distributions including beta, gamma, uniform, normal and lognormal.

The program demonstrates estimation procedures used in recurrent event data analysis (Homogeneous and Nonhomogeneous Poisson Processes as well as Reliability Growth modeling).