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generate initial design with design points and the approximate allocation", "topics": [ "design_initial_self" ] }, { "page": "discrete_rv_self", "title": "function to generate discrete uniform random variables for initial random design points in ForLion", "topics": [ "discrete_rv_self" ] }, { "page": "dprime_func_self", "title": "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)", "topics": [ "dprime_func_self" ] }, { "page": "EW_design_initial_GLM", "title": "function to generate a initial EW Design for generalized linear models", "topics": [ "EW_design_initial_GLM" ] }, { "page": "EW_design_initial_MLM", "title": "function to generate a initial EW Design for multinomial logistic models", "topics": [ "EW_design_initial_MLM" ] }, { "page": "EW_dprime_func_self", "title": "function to calculate dEu/dx in the gradient of d(x, Xi), will be used in EW_ForLion_MLM_func() function", "topics": [ "EW_dprime_func_self" ] }, { "page": "EW_Fi_MLM_func", "title": "function to generate the expected fisher information at one design point xi for multinomial logit models", "topics": [ "EW_Fi_MLM_func" ] }, { "page": "EW_ForLion_GLM_Optimal", "title": "EW ForLion for generalized linear models", "topics": [ "EW_ForLion_GLM_Optimal" ] }, { "page": "EW_ForLion_MLM_Optimal", "title": "EW ForLion function for multinomial logit models", "topics": [ "EW_ForLion_MLM_Optimal" ] }, { "page": "EW_liftoneDoptimal_GLM_func", "title": "EW Lift-one algorithm for D-optimal approximate design", "topics": [ "EW_liftoneDoptimal_GLM_func" ] }, { "page": "EW_liftoneDoptimal_log_GLM_func", "title": "EW Lift-one algorithm for D-optimal approximate design in log scale", "topics": [ "EW_liftoneDoptimal_log_GLM_func" ] }, { "page": "EW_liftoneDoptimal_MLM_func", "title": "function of EW liftone for multinomial logit model", "topics": [ "EW_liftoneDoptimal_MLM_func" ] }, { "page": "EW_Xw_maineffects_self", "title": "function for calculating X=h(x) and E_w=E(nu(beta^T h(x))) given a design point x=(1,x1,...,xd)^T", "topics": [ "EW_Xw_maineffects_self" ] }, { "page": "Fi_MLM_func", "title": "function to generate fisher information at one design point xi for multinomial logit models", "topics": [ "Fi_MLM_func" ] }, { "page": "ForLion_GLM_Optimal", "title": "ForLion for generalized linear models", "topics": [ "ForLion_GLM_Optimal" ] }, { "page": "ForLion_MLM_Optimal", "title": "ForLion function for multinomial logit models", "topics": [ "ForLion_MLM_Optimal" ] }, { "page": "GLM_Exact_Design", "title": "rounding algorithm for generalized linear models", "topics": [ "GLM_Exact_Design" ] }, { "page": "liftoneDoptimal_GLM_func", "title": "Lift-one algorithm for D-optimal approximate design", "topics": [ "liftoneDoptimal_GLM_func" ] }, { "page": "liftoneDoptimal_log_GLM_func", "title": "Lift-one algorithm for D-optimal approximate design in log scale", "topics": [ 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