Optimal Designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm (ICA)

---

Documentation for package ‘ICAOD’ version 1.0.1

• DESCRIPTION file.

Help Pages

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
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 Imperialist Competitive Algorithm
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'