Package: ICAOD 1.0.2

ICAOD: Optimal Designs for Nonlinear Models via ICA

Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2022) <doi:10.32614/RJ-2022-043>, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

Authors:Ehsan Masoudi [aut, cre], Heinz Holling [aut], Weng Kee Wong [aut], Seongho Kim [ctb]

ICAOD_1.0.2.tar.gz
ICAOD_1.0.2.zip(r-4.7)ICAOD_1.0.2.zip(r-4.6)ICAOD_1.0.2.zip(r-4.5)
ICAOD_1.0.2.tgz(r-4.6-x86_64)ICAOD_1.0.2.tgz(r-4.6-arm64)ICAOD_1.0.2.tgz(r-4.5-x86_64)ICAOD_1.0.2.tgz(r-4.5-arm64)
ICAOD_1.0.2.tar.gz(r-4.7-arm64)ICAOD_1.0.2.tar.gz(r-4.7-x86_64)ICAOD_1.0.2.tar.gz(r-4.6-arm64)ICAOD_1.0.2.tar.gz(r-4.6-x86_64)
ICAOD_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ICAOD/json (API)
NEWS

# Install 'ICAOD' in R:
install.packages('ICAOD', repos = c('https://ehsan66.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.51 score 32 scripts 200 downloads 1 mentions 41 exports 17 dependencies

Last updated from:5bc90a0f75. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK175
linux-devel-x86_64OK187
source / vignettesOK216
linux-release-arm64OK183
linux-release-x86_64OK172
macos-release-arm64OK169
macos-release-x86_64OK301
macos-oldrel-arm64OK142
macos-oldrel-x86_64OK264
windows-develOK188
windows-releaseOK228
windows-oldrelOK184
wasm-releaseOK131

Exports:bayesbayes.updatebayescompbeffcrt.bayes.controlcrt.minimax.controlFIM_2par_exp_censor1FIM_2par_exp_censor2FIM_3par_exp_censor1FIM_3par_exp_censor2FIM_exp_2parFIM_kinetics_alcoholFIM_logisticFIM_logistic_2predFIM_logistic_4parFIM_loglinFIM_mixed_inhibitionFIM_power_logisticFIM_sig_emaxICA.controllefflocallylocallycompmeffminimaxmultiplenormalrobustsens.bayes.controlsens.controlsens.minimax.controlsensbayessensbayescompsenslocallysenslocallycompsensminimaxsensmultiplesensrobustskewnormalstudentuniform

Dependencies:cubaturedata.tablelatticeMASSMatrixMatrixModelsmnormtmvQuadnloptrnumDerivquantregRcppRcppEigensnSparseMstatmodsurvival

Readme and manuals

Help Manual

Help pageTopics
Bayesian D-Optimal Designsbayes
Updating an Object of Class 'minimax'bayes.update
Bayesian Compound DP-Optimal Designsbayescomp
Calculates Relative Efficiency for Bayesian Optimal Designsbeff
Returns Control Parameters for Approximating Bayesian Criteriacrt.bayes.control
Returns Control Parameters for Optimizing Minimax Criteria Over The Parameter Spacecrt.minimax.control
Fisher Information Matrix for a 2-Parameter Cox Proportional-Hazards Model for Type One Censored DataFIM_2par_exp_censor1
Fisher Information Matrix for a 2-Parameter Cox Proportional-Hazards Model for Random Censored DataFIM_2par_exp_censor2
Fisher Information Matrix for a 3-Parameter Cox Proportional-Hazards Model for Type One Censored DataFIM_3par_exp_censor1
Fisher Information Matrix for a 3-Parameter Cox Proportional-Hazards Model for Random Censored DataFIM_3par_exp_censor2
Fisher Information Matrix for the 2-Parameter Exponential ModelFIM_exp_2par
Fisher Information Matrix for the Alcohol-Kinetics ModelFIM_kinetics_alcohol
Fisher Information Matrix for the 2-Parameter Logistic (2PL) ModelFIM_logistic
Fisher Information Matrix for the Logistic Model with Two PredictorsFIM_logistic_2pred
Fisher Information Matrix for the 4-Parameter Logistic ModelFIM_logistic_4par
Fisher Information Matrix for the Mixed Inhibition ModelFIM_loglin
Fisher Information Matrix for the Mixed Inhibition Model.FIM_mixed_inhibition
Fisher Information Matrix for the Power Logistic ModelFIM_power_logistic
Fisher Information Matrix for the Sigmoid Emax ModelFIM_sig_emax
Returns ICA Control Optimization ParametersICA.control
ICAOD: Finding Optimal Designs for Nonlinear Models Using the Imperialist Competitive AlgorithmICAOD-package ICAOD
Calculates Relative Efficiency for Locally Optimal Designsleff
Locally D-Optimal Designslocally
Locally DP-Optimal Designslocallycomp
Calculates Relative Efficiency for Minimax Optimal Designsmeff
Minimax and Standardized Maximin D-Optimal Designsminimax
Locally Multiple Objective Optimal Designs for the 4-Parameter Hill Modelmultiple
Assumes A Multivariate Normal Prior Distribution for The Model Parametersnormal
Plotting 'minimax' Objectsplot.minimax
Printing 'minimax' Objectsprint.minimax
Printing 'sensminimax' Objectsprint.sensminimax
Robust D-Optimal Designsrobust
Returns Control Parameters for Approximating The Integrals In The Bayesian Sensitivity Functionssens.bayes.control
Returns Control Parameters To Find Maximum of The Sensitivity (Derivative) Function Over The Design Spacesens.control
Returns Control Parameters for Verifying General Equivalence Theorem For Minimax Optimal Designssens.minimax.control
Verifying Optimality of Bayesian D-optimal Designssensbayes
Verifying Optimality of Bayesian Compound DP-optimal Designssensbayescomp
Verifying Optimality of The Locally D-optimal Designssenslocally
Verifying Optimality of The Locally DP-optimal Designssenslocallycomp
Verifying Optimality of The Minimax and Standardized maximin D-optimal Designssensminimax
Verifying Optimality of The Multiple Objective Designs for The 4-Parameter Hill Modelsensmultiple
Verifying Optimality of The Robust Designssensrobust
Assumes A Multivariate Skewed Normal Prior Distribution for The Model Parametersskewnormal
Multivariate Student's t Prior Distribution for Model Parametersstudent
Assume A Multivariate Uniform Prior Distribution for The Model Parametersuniform
Updating an Object of Class 'minimax'update.minimax