FIM_2par_exp_censor1    Fisher Information Matrix for a 2-Parameter Cox
                        Proportional-Hazards Model for Type One
                        Censored Data
FIM_2par_exp_censor2    Fisher Information Matrix for a 2-Parameter Cox
                        Proportional-Hazards Model for Random Censored
                        Data
FIM_3par_exp_censor1    Fisher Information Matrix for a 3-Parameter Cox
                        Proportional-Hazards Model for Type One
                        Censored Data
FIM_3par_exp_censor2    Fisher Information Matrix for a 3-Parameter Cox
                        Proportional-Hazards Model for Random Censored
                        Data
FIM_exp_2par            Fisher Information Matrix for the 2-Parameter
                        Exponential Model
FIM_kinetics_alcohol    Fisher Information Matrix for the
                        Alcohol-Kinetics Model
FIM_logistic            Fisher Information Matrix for the 2-Parameter
                        Logistic (2PL) Model
FIM_logistic_2pred      Fisher Information Matrix for the Logistic
                        Model with Two Predictors
FIM_logistic_4par       Fisher Information Matrix for the 4-Parameter
                        Logistic Model
FIM_loglin              Fisher Information Matrix for the Mixed
                        Inhibition Model
FIM_mixed_inhibition    Fisher Information Matrix for the Mixed
                        Inhibition Model.
FIM_power_logistic      Fisher Information Matrix for the Power
                        Logistic Model
FIM_sig_emax            Fisher Information Matrix for the Sigmoid Emax
                        Model
ICA.control             Returns ICA Control Optimization Parameters
ICAOD                   ICAOD: Finding Optimal Designs for Nonlinear
                        Models Using the Imperialist Competitive
                        Algorithm
bayes                   Bayesian D-Optimal Designs
bayes.update            Updating an Object of Class 'minimax'
bayescomp               Bayesian Compound DP-Optimal Designs
beff                    Calculates Relative Efficiency for Bayesian
                        Optimal Designs
crt.bayes.control       Returns Control Parameters for Approximating
                        Bayesian Criteria
crt.minimax.control     Returns Control Parameters for Optimizing
                        Minimax Criteria Over The Parameter Space
leff                    Calculates Relative Efficiency for Locally
                        Optimal Designs
locally                 Locally D-Optimal Designs
locallycomp             Locally DP-Optimal Designs
meff                    Calculates Relative Efficiency for Minimax
                        Optimal Designs
minimax                 Minimax and Standardized Maximin D-Optimal
                        Designs
multiple                Locally Multiple Objective Optimal Designs for
                        the 4-Parameter Hill Model
normal                  Assumes A Multivariate Normal Prior
                        Distribution for The Model Parameters
plot.minimax            Plotting 'minimax' Objects
print.minimax           Printing 'minimax' Objects
print.sensminimax       Printing 'sensminimax' Objects
robust                  Robust D-Optimal Designs
sens.bayes.control      Returns Control Parameters for Approximating
                        The Integrals In The Bayesian Sensitivity
                        Functions
sens.control            Returns Control Parameters To Find Maximum of
                        The Sensitivity (Derivative) Function Over The
                        Design Space
sens.minimax.control    Returns Control Parameters for Verifying
                        General Equivalence Theorem For Minimax Optimal
                        Designs
sensbayes               Verifying Optimality of Bayesian D-optimal
                        Designs
sensbayescomp           Verifying Optimality of Bayesian Compound
                        DP-optimal Designs
senslocally             Verifying Optimality of The Locally D-optimal
                        Designs
senslocallycomp         Verifying Optimality of The Locally DP-optimal
                        Designs
sensminimax             Verifying Optimality of The Minimax and
                        Standardized maximin D-optimal Designs
sensmultiple            Verifying Optimality of The Multiple Objective
                        Designs for The 4-Parameter Hill Model
sensrobust              Verifying Optimality of The Robust Designs
skewnormal              Assumes A Multivariate Skewed Normal Prior
                        Distribution for The Model Parameters
student                 Multivariate Student's t Prior Distribution for
                        Model Parameters
uniform                 Assume A Multivariate Uniform Prior
                        Distribution for The Model Parameters
update.minimax          Updating an Object of Class 'minimax'
