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ICAOD (version 0.9.9)

Optimal Designs for Nonlinear Models

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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Install

install.packages('ICAOD')

Monthly Downloads

277

Version

0.9.9

License

GPL (>= 2)

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Repository

https://github.com/ehsan66/ICAOD

Maintainer

Ehsan Masoudi

Last Published

April 19th, 2019

Functions in ICAOD (0.9.9)

Fisher Information Matrix for the 2-Parameter Logistic (2PL) Model

crt.bayes.control

Returns Control Parameters for Approximating Bayesian Criteria

crt.minimax.control

Returns Control Parameters for Optimizing Minimax Criteria Over The Parameter Space

Calculates Relative Efficiency for Locally Optimal Designs

Locally D-Optimal Designs

FIM_logistic_4par

Fisher Information Matrix for the 4-Parameter Logistic Model

Fisher Information Matrix for the Mixed Inhibition Model

Bayesian Compound DP-Optimal Designs

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

FIM_logistic_2pred

Fisher Information Matrix for the Logistic Model with Two Predictors

sens.bayes.control

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

Calculates Relative Efficiency for Bayesian Optimal Designs

Bayesian D-Optimal Designs

Assumes A Multivariate Skewed Normal Prior Distribution for The Model Parameters

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

Multivariate Student's t Prior Distribution for Model Parameters

FIM_2par_exp_censor1

Fisher Information Matrix for a 2-Parameter Cox Proportional-Hazards Model for Type One Censored Data

Fisher Information Matrix for the Sigmoid Emax Model

Assumes A Multivariate Normal Prior Distribution for The Model Parameters

Returns ICA Control Optimization Parameters

Plotting bayes Objects

senslocallycomp

Verifying Optimality of The Locally DP-optimal Designs

Verifying Optimality of The Minimax and Standardized maximin D-optimal Designs

Plotting minimax Objects

Locally DP-Optimal Designs

Printing minimax Objects

FIM_2par_exp_censor2

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

Printing bayes Objects

Calculates Relative Efficiency for Minimax Optimal Designs

sens.minimax.control

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

Updating an Object of Class bayes

Assume A Multivariate Uniform Prior Distribution for The Model Parameters

Verifying Optimality of Bayesian D-optimal Designs

print.sensminimax

Printing sensminimax Objects

print.sensbayes

Printing sensbayes Objects

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

Robust D-Optimal Designs

Updating an Object of Class minimax

FIM_mixed_inhibition

Fisher Information Matrix for the Mixed Inhibition Model.

FIM_power_logistic

Fisher Information Matrix for the Power Logistic Model

Verifying Optimality of The Robust Designs

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

Minimax and Standardized Maximin D-Optimal Designs

Verifying Optimality of Bayesian Compound DP-optimal Designs

Verifying Optimality of The Locally D-optimal Designs

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

Fisher Information Matrix for the 2-Parameter Exponential Model

FIM_kinetics_alcohol

Fisher Information Matrix for the Alcohol-Kinetics Model