Calculate the optimal sample size allocation that 
    uses the minimum resources to achieve targeted statistical 
    power in experiments.
    Perform power analyses with and without accommodating 
    costs and budget. The designs cover single-level 
    and multilevel experiments detecting main, mediation, 
    and moderation effects (and some combinations). 
    The references for the proposed methods include: 
    (1) Shen, Z., & Kelcey, B. (2020). 
    Optimal sample allocation under unequal costs in 
    cluster-randomized trials.
    Journal of Educational and Behavioral Statistics, 45(4): 446-474.
        <doi:10.3102/1076998620912418>.
    (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for
    three-level multisite cluster-randomized trials. 
    Journal of Research on Educational Effectiveness, 15 (1), 130-150.
    <doi:10.1080/19345747.2021.1953200>.
    (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in
    multisite randomized trials. The Journal of Experimental Education,
    90(3), 693-711. <doi:10.1080/00220973.2020.1830361>.
    (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). 
    Using ant colony optimization to identify optimal sample 
    allocations in cluster-randomized trials. 
    The Journal of Experimental Education, 93(1), 167-185.
    <doi:10.1080/00220973.2024.2306392>.
    (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and 
    optimal design for randomized controlled trials investigating 
    mediation effects. Psychological Methods.
     <doi:10.1037/met0000698>.
    (6) Champely, S. (2020). pwr: Basic functions for power analysis 
    (Version 1.3-0) [Software]. Available from 
    <https://CRAN.R-project.org/package=pwr>.

The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001) <DOI:10.1023/A:1010933404324> respectively.