Provides generic data structures and algorithms for use with forest
    mensuration data in a consistent framework. The functions and objects
    included are a collection of broadly applicable tools. More specialized 
    applications should be implemented in separate packages that build on this
    foundation. Documentation about 'ForestElementsR' is provided by three
    vignettes included in this package. For an introduction to the field of 
    forest mensuration, refer to the textbooks by Kershaw et al. (2017)
    <doi:10.1002/9781118902028>, and van Laar and Akca (2007)
    <doi:10.1007/978-1-4020-5991-9>.

Estimates the conditional error distributions of random forest
    predictions and common parameters of those distributions, including
    conditional misclassification rates, conditional mean squared prediction
    errors, conditional biases, and conditional quantiles, by out-of-bag
    weighting of out-of-bag prediction errors as proposed by Lu and Hardin
    (2021). This package is compatible with several existing packages that
    implement random forests in R.

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.