Computing Optimal Cutpoints in Diagnostic Tests
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- and IV-optimal designs of experiments. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <www.gurobi.com> and <http://www.gurobi.com/documentation>, respectively.

Algorithms for D-, A-, and IV-Optimal Designs
Algorithms for D-, A- and IV-optimal designs of experiments. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <www.gurobi.com> and the file gurobi_inst.txt, respectively.

A Toolbox for Computing Efficient Designs of Experiments
Algorithms for D-, A-, I-, and c-optimal designs. 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.
grDevices, graphics, Matrix, lpSolve, matrixStats, matrixcalc,
plyr, quadprog, rgl, stats, utils

Adam Kapelner, Michael Sklar, Abba M. Krieger and David Azriel
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>.
R (>= 3.2.0), ggplot2 (>= 3.0), momentchi2 (>= 0.1.5),
GreedyExperimentalDesign (>= 1.3)