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ICAOD (version 0.9.8)

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) .

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Version

Install

install.packages('ICAOD')

Monthly Downloads

277

Version

0.9.8

License

GPL (>= 2)

Issues

1

Pull Requests

0

Stars

1

Forks

0

Maintainer

Ehsan Masoudi

Last Published

January 14th, 2019

Functions in ICAOD (0.9.8)

sens.bayes.control

Control Parameters for Verifying General Equivalence Theorem for Bayesian Designs
plot.minimax

Plotting minimax Objects
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_logistic

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

Multivariate Student's t Prior Distribution for Model Parameters
FIM_exp_2par

Fisher Information Matrix for the 2-Parameter Exponential Model
FIM_kinetics_alcohol

Fisher Information Matrix for the Alcohol-Kinetics Model
print.minimax

Printing minimax Objects
FIM_2par_exp_censor2

Fisher Information Matrix for a 2-Parameter Cox Proportional-Hazards Model for Random Censored Data
FIM_logistic_2pred

Fisher Information Matrix for the Logistic Model with Two Predictors
normal

Assumes A Multivariate Normal Prior Distribution for The Model Parameters
FIM_logistic_4par

Fisher Information Matrix for the 4-Parameter Logistic Model
plot.bayes

Plotting bayes Objects
senslocally

Verifying Optimality of The Locally D-optimal Designs
sensbayes

Verifying Optimality of Bayesian D-optimal Designs
sensbayescomp

Verifying Optimality of Bayesian Compound DP-optimal Designs
bayescomp

Bayesian Compound DP-Optimal Designs
beff

Calculates Relative Efficiency for Bayesian Optimal Designs
senslocallycomp

Verifying Optimality of The Locally DP-optimal Designs
print.bayes

Printing bayes Objects
print.sensbayes

Printing sensbayes Objects
ICAOD

ICAOD: Finding Optimal Designs for Nonlinear Models
FIM_sig_emax

Fisher Information Matrix for the Sigmoid Emax Model
FIM_loglin

Fisher Information Matrix for the Mixed Inhibition Model
sensrobust

Verifying Optimality of The Robust Designs
locally

Locally D-Optimal Designs
bayes

Bayesian D-Optimal Designs
crt.bayes.control

Control Parameters for Approximating Bayesian Criteria
FIM_mixed_inhibition

Fisher Information Matrix for the Mixed Inhibition Model.
iterate.minimax

Updating an Object of Class minimax
leff

Calculates Relative Efficiency for Locally Optimal Designs
uniform

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

Control Parameters for Optimizing Minimax Criteria Over The Parameter Space
FIM_power_logistic

Fisher Information Matrix for the Power Logistic Model
ICA.control

ICA Control Optimization Parameters
minimax

Minimax and Standardized Maximin D-Optimal Designs
multiple

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

Updating an Object of Class 'bayes' or 'minimax'
locallycomp

Locally DP-Optimal Designs
sensmultiple

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

Printing sensminimax Objects
robust

Robust D-Optimal Designs
sensminimax

Verifying Optimality of The Minimax and Standardized maximin D-optimal Designs
sens.minimax.control

Control Parameters for Verifying General Equivalence Theorem
skewnormal

Assumes A Multivariate Skewed Normal Prior Distribution for The Model Parameters
iterate.bayes

Updating an Object of Class bayes