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 univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear
			 regression analysis when error term follows skewed t distribution, 10 ) Estimating 
			 parameters of a given distribution fitted to grouped data using method of maximum
			 likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through 
			 the Bayesian, method of moment, conditional maximum likelihood, and two - percentile 
			 method.

Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps. Details were published in Silva and others (2019) <doi:10.1111/2041-210X.13211>.

The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.