| design_initial_self | function to generate initial design with design points and the approximate allocation |
| discrete_rv_self | function to generate discrete uniform random variables for initial random design points in ForLion |
| dprime_func_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) |
| EW_design_initial_GLM | function to generate a initial EW Design for generalized linear models |
| EW_design_initial_MLM | function to generate a initial EW Design for multinomial logistic models |
| EW_dprime_func_self | function to calculate dEu/dx in the gradient of d(x, Xi), will be used in EW_ForLion_MLM_func() function |
| EW_Fi_MLM_func | function to generate the expected fisher information at one design point xi for multinomial logit models |
| EW_ForLion_GLM_Optimal | EW ForLion for generalized linear models |
| EW_ForLion_MLM_Optimal | EW ForLion function for multinomial logit models |
| EW_liftoneDoptimal_GLM_func | EW Lift-one algorithm for D-optimal approximate design |
| EW_liftoneDoptimal_log_GLM_func | EW Lift-one algorithm for D-optimal approximate design in log scale |
| EW_liftoneDoptimal_MLM_func | function of EW liftone for multinomial logit model |
| EW_Xw_maineffects_self | function for calculating X=h(x) and E_w=E(nu(beta^T h(x))) given a design point x=(1,x1,...,xd)^T |
| Fi_MLM_func | function to generate fisher information at one design point xi for multinomial logit models |
| ForLion_GLM_Optimal | ForLion for generalized linear models |
| ForLion_MLM_Optimal | ForLion function for multinomial logit models |
| GLM_Exact_Design | rounding algorithm for generalized linear models |
| liftoneDoptimal_GLM_func | Lift-one algorithm for D-optimal approximate design |
| liftoneDoptimal_log_GLM_func | Lift-one algorithm for D-optimal approximate design in log scale |
| liftoneDoptimal_MLM_func | function of liftone for multinomial logit model |
| MLM_Exact_Design | rounding algorithm for multinomial logit models |
| nu1_cauchit_self | function to calculate first derivative of nu function given eta for cauchit link |
| nu1_identity_self | function to calculate first derivative of nu function given eta for identity link |
| nu1_logit_self | function to calculate the first derivative of nu function given eta for logit link |
| nu1_loglog_self | function to calculate the first derivative of nu function given eta for log-log link |
| nu1_log_self | function to calculate first derivative of nu function given eta for log link |
| nu1_probit_self | function to calculate the first derivative of nu function given eta for probit link |
| nu2_cauchit_self | function to calculate the second derivative of nu function given eta for cauchit link |
| nu2_identity_self | function to calculate the second derivative of nu function given eta for identity link |
| nu2_logit_self | function to calculate the second derivative of nu function given eta for logit link |
| nu2_loglog_self | function to calculate the second derivative of nu function given eta for loglog link |
| nu2_log_self | function to calculate the second derivative of nu function given eta for log link |
| nu2_probit_self | function to calculate the second derivative of nu function given eta for probit link |
| nu_cauchit_self | function to calculate w = nu(eta) given eta for cauchit link |
| nu_identity_self | function to calculate w = nu(eta) given eta for identity link |
| nu_logit_self | function to calculate w = nu(eta) given eta for logit link |
| nu_loglog_self | function to calculate w = nu(eta) given eta for loglog link |
| nu_log_self | function to calculate w = nu(eta) given eta for log link |
| nu_probit_self | function to calculate w = nu(eta) given eta for probit link |
| polynomial_sol_J3 | functions to solve 2th order polynomial function given coefficients |
| polynomial_sol_J4 | functions to solve 3th order polynomial function given coefficients |
| polynomial_sol_J5 | functions to solve 4th order polynomial function given coefficients |
| print.design_output | Print Method for Design Output from ForLion Algorithms |
| print.list_output | Print Method for list_output Objects |
| svd_inverse | SVD Inverse Of A Square Matrix |
| xmat_discrete_self | Generate initial designs within ForLion algorithms |
| Xw_maineffects_self | function for calculating X=h(x) and w=nu(beta^T h(x)) given a design point x = (x1,...,xd)^T |