A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.

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>.