Parse options from the command-line using a simple, clean syntax. 
    It requires little or no specification and supports short and long options,
    GNU-, Java- or Microsoft- style syntaxes, verb commands and more.

A set of standard benchmark optimization functions for R and
    a common interface to sample them.

Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.

Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.

Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software <doi:10.18637/jss.v114.i10>.

This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based 
	on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed.
	Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices.
	Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments"
	Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arXiv:1905.03337>.