Constrained Sampling in Paid Research Studies
Convert the approximate allocation (proportion) to exact allocation (i...
Find (constrained) uniform exact allocation of the study for bounded d...
Fisher information matrix of generalized linear model (GLM)
The Fisher information matrix of multinomial logistic model (MLM)
Determinant of Fisher information matrix for GLM
Determinant of Fisher information matrix of multinomial logistic model...
Determinant function to be used for finding constrained uniform sampli...
Convert the approximate allocation (proportion) to exact allocation (i...
Generate Fisher information matrix F_x at a design point x_i for Multi...
trauma_data example (see Huang, Tong, Yang (2023)) specific function f...
trial_data example (see Huang, Tong, Yang (2023)) specific function fo...
Find constrained D-optimal approximate design for generalized linear m...
Find constrained D-optimal designs for Multinomial Logit Models (MLM)
Unconstrained lift-one algorithm to find D-optimal allocations for GLM
Unconstrained lift-one algorithm to find D-optimal allocations for MLM
Print Method for list_output Objects
Print Method for matrix_list Objects
Print Method for matrix_output Objects
Calculate the diagonal elements nu of Fisher information matrix
In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the 'CDsampling' package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025)<doi:10.5705/ss.202022.0414>.