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ICAOD (version 1.0.1)

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

Description

Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2017) and Masoudi et al. (2019) .

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Version

Install

install.packages('ICAOD')

Monthly Downloads

252

Version

1.0.1

License

GPL (>= 2)

Maintainer

Ehsan Masoudi

Last Published

October 11th, 2020

Functions in ICAOD (1.0.1)

FIM_loglin

Fisher Information Matrix for the Mixed Inhibition Model
beff

Calculates Relative Efficiency for Bayesian Optimal Designs
FIM_sig_emax

Fisher Information Matrix for the Sigmoid Emax Model
bayes.update

Updating an Object of Class minimax
FIM_power_logistic

Fisher Information Matrix for the Power Logistic Model
FIM_mixed_inhibition

Fisher Information Matrix for the Mixed Inhibition Model.
ICAOD

ICAOD: Finding Optimal Designs for Nonlinear Models Using Imperialist Competitive Algorithm
bayescomp

Bayesian Compound DP-Optimal Designs
crt.bayes.control

Returns Control Parameters for Approximating Bayesian Criteria
ICA.control

Returns ICA Control Optimization Parameters
bayes

Bayesian D-Optimal Designs
crt.minimax.control

Returns Control Parameters for Optimizing Minimax Criteria Over The Parameter Space
leff

Calculates Relative Efficiency for Locally Optimal Designs
minimax

Minimax and Standardized Maximin D-Optimal Designs
meff

Calculates Relative Efficiency for Minimax Optimal Designs
locally

Locally D-Optimal Designs
locallycomp

Locally DP-Optimal Designs
print.minimax

Printing minimax Objects
plot.minimax

Plotting minimax Objects
multiple

Locally Multiple Objective Optimal Designs for the 4-Parameter Hill Model
normal

Assumes A Multivariate Normal Prior Distribution for The Model Parameters
robust

Robust D-Optimal Designs
print.sensminimax

Printing sensminimax Objects
sens.minimax.control

Returns Control Parameters for Verifying General Equivalence Theorem For Minimax Optimal Designs
sensbayes

Verifying Optimality of Bayesian D-optimal Designs
sens.control

Returns Control Parameters To Find Maximum of The Sensitivity (Derivative) Function Over The Design Space
sens.bayes.control

Returns Control Parameters for Approximating The Integrals In The Bayesian Sensitivity Functions
sensmultiple

Verifying Optimality of The Multiple Objective Designs for The 4-Parameter Hill Model
sensrobust

Verifying Optimality of The Robust Designs
student

Multivariate Student's t Prior Distribution for Model Parameters
skewnormal

Assumes A Multivariate Skewed Normal Prior Distribution for The Model Parameters
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
uniform

Assume A Multivariate Uniform Prior Distribution for The Model Parameters
update.minimax

Updating an Object of Class minimax
FIM_logistic_4par

Fisher Information Matrix for the 4-Parameter Logistic Model
FIM_2par_exp_censor2

Fisher Information Matrix for a 2-Parameter Cox Proportional-Hazards Model for Random Censored Data
FIM_exp_2par

Fisher Information Matrix for the 2-Parameter Exponential 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_3par_exp_censor1

Fisher Information Matrix for a 3-Parameter Cox Proportional-Hazards Model for Type One Censored Data
FIM_2par_exp_censor1

Fisher Information Matrix for a 2-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_kinetics_alcohol

Fisher Information Matrix for the Alcohol-Kinetics Model