Package: ForLion 0.1.0

ForLion: 'ForLion' Algorithm to Find D-Optimal Designs for Experiments

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the 'ForLion' algorithm and 'EW ForLion' algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.

Authors:Yifei Huang [aut], Siting Lin [aut, cre], Jie Yang [aut]

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ForLion.pdf |ForLion.html✨
ForLion/json (API)

# Install 'ForLion' in R:
install.packages('ForLion', repos = c('https://lin-siting.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

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

2.70 score 41 exports 7 dependencies

Last updated 28 days agofrom:5ba4a13cba. Checks:8 OK. Indexed: yes.

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Doc / VignettesOKFeb 12 2025
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R-4.5-linuxOKFeb 12 2025
R-4.4-winOKFeb 12 2025
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R-4.3-macOKFeb 12 2025

Exports:design_initial_selfdiscrete_rv_selfdprime_func_selfEW_design_initial_selfEW_dprime_func_selfEW_Fi_MLM_funcEW_ForLion_GLM_OptimalEW_ForLion_MLM_OptimalEW_liftoneDoptimal_GLM_funcEW_liftoneDoptimal_log_GLM_funcEW_liftoneDoptimal_MLM_funcEW_Xw_maineffects_selfFi_MLM_funcForLion_GLM_OptimalForLion_MLM_OptimalGLM_Exact_DesignliftoneDoptimal_GLM_funcliftoneDoptimal_log_GLM_funcliftoneDoptimal_MLM_funcMLM_Exact_Designnu_cauchit_selfnu_identity_selfnu_log_selfnu_logit_selfnu_loglog_selfnu_probit_selfnu1_cauchit_selfnu1_identity_selfnu1_log_selfnu1_logit_selfnu1_loglog_selfnu1_probit_selfnu2_cauchit_selfnu2_identity_selfnu2_log_selfnu2_logit_selfnu2_loglog_selfnu2_probit_selfsvd_inversexmat_discrete_selfXw_maineffects_self

Dependencies:cubatureGPArotationlatticemnormtnlmepsychRcpp

Introduction to ForLion package

Rendered fromIntro_to_ForLion.Rmdusingknitr::rmarkdownon Feb 12 2025.

Last update: 2025-02-11
Started: 2025-02-11

Readme and manuals

Help Manual

Help pageTopics
function to generate random initial design with design points and the approximate allocationdesign_initial_self
function to generate discrete uniform random variables for initial random design points in ForLiondiscrete_rv_self
Function to calculate du/dx in the gradient of d(x, Xi), will be used in ForLion_MLM_func() function, details see Appendix C in Huang, Li, Mandal, Yang (2024)dprime_func_self
function to generate random initial design with design points and the approximate allocation (For EW)EW_design_initial_self
Function to calculate dEu/dx in the gradient of d(x, Xi), will be used in EW_ForLion_MLM_func() functionEW_dprime_func_self
Function to generate the Expectation of fisher information at one design point xi for multinomial logit modelsEW_Fi_MLM_func
EW ForLion for generalized linear modelsEW_ForLion_GLM_Optimal
EW ForLion function for multinomial logit modelsEW_ForLion_MLM_Optimal
EW Lift-one algorithm for D-optimal approximate designEW_liftoneDoptimal_GLM_func
EW Lift-one algorithm for D-optimal approximate design in log scaleEW_liftoneDoptimal_log_GLM_func
function of EW liftone for multinomial logit modelEW_liftoneDoptimal_MLM_func
function for calculating X=h(x) and E_w=E(nu(beta^T h(x))) give a design point x=(1,x1,...,xd)^TEW_Xw_maineffects_self
Function to generate fisher information at one design point xi for multinomial logit modelsFi_MLM_func
ForLion for generalized linear modelsForLion_GLM_Optimal
ForLion function for multinomial logit modelsForLion_MLM_Optimal
Approximation to exact design algorithm for generalized linear modelGLM_Exact_Design
Lift-one algorithm for D-optimal approximate designliftoneDoptimal_GLM_func
Lift-one algorithm for D-optimal approximate design in log scaleliftoneDoptimal_log_GLM_func
function of liftone for multinomial logit modelliftoneDoptimal_MLM_func
Approximation to exact design algorithm for multinomial logit modelMLM_Exact_Design
function to calculate w = nu(eta) given eta for cauchit linknu_cauchit_self
Function to calculate w = nu(eta) given eta for identity linknu_identity_self
Function to calculate w = nu(eta) given eta for log linknu_log_self
function to calculate w = nu(eta) given eta for logit linknu_logit_self
function to calculate w = nu(eta) given eta for loglog linknu_loglog_self
function to calculate w = nu(eta) given eta for probit linknu_probit_self
Function to calculate first derivative of nu function given eta for cauchit linknu1_cauchit_self
function to calculate first derivative of nu function given eta for identity linknu1_identity_self
function to calculate first derivative of nu function given eta for log linknu1_log_self
function to calculate the first derivative of nu function given eta for logit linknu1_logit_self
function to calculate the first derivative of nu function given eta for log-log linknu1_loglog_self
function to calculate the first derivative of nu function given eta for probit linknu1_probit_self
function to calculate the second derivative of nu function given eta for cauchit linknu2_cauchit_self
function to calculate the second derivative of nu function given eta for identity linknu2_identity_self
function to calculate the second derivative of nu function given eta for log linknu2_log_self
function to calculate the second derivative of nu function given eta for logit linknu2_logit_self
function to calculate the second derivative of nu function given eta for loglog linknu2_loglog_self
function to calculate the second derivative of nu function given eta for probit linknu2_probit_self
Print Method for Design Output from ForLion Algorithmprint.design_output
Print Method for list_output Objectsprint.list_output
SVD Inverse Of A Square Matrix This function returns the inverse of a matrix using singular value decomposition. If the matrix is a square matrix, this should be equivalent to using the solve function. If the matrix is not a square matrix, then the result is the Moore-Penrose pseudo inverse.svd_inverse
Generate GLM random initial designs within ForLion algorithmxmat_discrete_self
function for calculating X=h(x) and w=nu(beta^T h(x)) given a design point x = (x1,...,xd)^TXw_maineffects_self