R: Optimal Designs for Nonlinear Models via ICA

# Optimal Designs for Nonlinear Models via ICA

## Documentation for package ‘ICAOD’ version 1.0.2

- DESCRIPTION file
- Package NEWS

## Help Pages

ICAOD-package | 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
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
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'