Interfacing to Nocedal et al. L-BFGS-B.3.0
        (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>)
        limited memory BFGS minimizer with bounds on parameters.
        This is a fork of 'lbfgsb3'.
	This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same
	function types and optimization as the optim() function (see writing 'R' extensions
        and source for details).  This package also adds more stopping criteria as well
        as allowing the adjustment of more tolerances.

Maximum likelihood estimation and likelihood ratio test are essential for modern statistics. This package supports in calculating likelihood based inference.
             Reference: Pawitan Y. (2001, ISBN:0-19-850765-8).

Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of 
    LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on 
    the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize 
    the model without losing any information, and fits the model on the standard fitting algorithm 
    with no convergence issues.

Implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.

The Length-Biased Power Garima distribution for computes the probability density,
    the cumulative density distribution and the quantile function of the distribution, 
    and generates sample values with random variables based on Kittipong and Sirinapa(2021)<DOI: 10.14456/sjst-psu.2021.89>.

Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.

Simulate expected equilibrium length composition, yield-per-recruit, and
    the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR
    model to length data to estimate  selectivity, relative apical fishing mortality, and
    the spawning potential ratio for data-limited fisheries.
    See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the
    LBSPR assessment method.