Developed for the following tasks. I ) Computing the probability density function,
cumulative distribution function, random generation, and estimating the parameters
of the eleven mixture models. II ) Point estimation of the parameters of two -
parameter Weibull distribution using twelve methods and three - parameter Weibull
distribution using nine methods. III ) The Bayesian estimators of the three -
parameter Weibull distribution. IV ) Estimating parameters of the three - parameter
Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to
grouped data using three methods including approximated maximum likelihood,
expectation maximization, and maximum likelihood. V ) Estimating the parameters
of the gamma, log-normal, and Weibull mixture models fitted to the grouped data
through the EM algorithm, VI ) Estimating parameters of the nonlinear height curve
fitted to the height - diameter observation, VII ) Estimating parameters, computing
probability density function, cumulative distribution function, and generating
realizations from gamma shape mixture model introduced by Venturini et al. (2008)
<doi:10.1214/07-AOAS156> , VIII ) Computing probability density function, cumulative
distribution function,	and generating realizations from four-parameter Johnson's SB distribution, and
VIIII ) Robust multiple linear regression analysis when error term follows skewed t distribution.

Developed for the following tasks. 1 ) Computing the probability density function,
cumulative distribution function, random generation, and estimating the parameters
of the eleven mixture models. 2 ) Point estimation of the parameters of two -
parameter Weibull distribution using twelve methods and three - parameter Weibull
distribution using nine methods. 3 ) The Bayesian inference for the three -
parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter
Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to
grouped data using three methods including approximated maximum likelihood,
expectation maximization, and maximum likelihood. 5 ) Estimating the parameters
of the gamma, log-normal, and Weibull mixture models fitted to the grouped data
through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve
fitted to the height - diameter observation, 7 ) Estimating parameters, computing
probability density function, cumulative distribution function, and generating
realizations from gamma shape mixture model introduced by Venturini et al. (2008)
<doi:10.1214/07-AOAS156> , 8 ) The Bayesian inference, computing probability density function,
cumulative distribution function, and generating realizations from four - parameter
Johnson SB distribution, and 9 ) Robust multiple linear regression analysis when error
term follows skewed t distribution.