A set of tools for evaluating several measures of case influence for structural equation models.

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.

Implements the adaptive influence-based borrowing framework 
    proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper 
    ``Improving Treatment Effect Estimation in Trials through Adaptive Borrowing 
    of External Controls" <doi:10.48550/arXiv.2604.13973> for augmenting Randomized Controlled 
    Trials (RCTs) with External Control (EC) data. This package provides a 
    comprehensive workflow to: (1) quantify the comparability of external control 
    samples using influence scores approximated via the influence function of the 
    M-estimator; (2) construct candidate borrowing subsets and select the optimal 
    subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic
    differences in external outcomes using R-learner methods implemented via 
    Ordinary Least Squares or Kernel Ridge Regression.

Provides functionality to compute various node centrality measures on networks.
    Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
    SNAP library), implementations of constraint and effective network size by Burt (2000) <doi:10.1016/S0191-3085(00)22009-1>; algorithm 
    to identify key players by Borgatti (2006) <doi:10.1007/s10588-006-7084-x>; and the 
    bridging algorithm by Valente and Fujimoto (2010) <doi:10.1016/j.socnet.2010.03.003>. 
    On Unix systems, the betweenness, Key Players, and
    bridging implementations are parallelized with OpenMP, which may run
    faster on systems which have OpenMP configured.