Entropy weighted k-means (ewkm) by Liping Jing, Michael
        K. Ng and Joshua Zhexue Huang (2007)
        <doi:10.1109/TKDE.2007.1048> is a weighted subspace clustering
        algorithm that is well suited to very high dimensional data.
        Weights are calculated as the importance of a variable with
        regard to cluster membership.  The two-level variable
        weighting clustering algorithm tw-k-means (twkm) by Xiaojun
        Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013)
        <doi:10.1109/TKDE.2011.262> introduces two types of weights,
        the weights on individual variables and the weights on
        variable groups, and they are calculated during the clustering
        process.  The feature group weighted k-means (fgkm) by Xiaojun
        Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012)
        <doi:10.1016/j.patcog.2011.06.004> extends this concept by
        grouping features and weighting the group in addition to
        weighting individual features.

Within-subject mediation analysis using structural equation modeling.
    Examine how changes in an outcome variable between two conditions are mediated 
    through one or more variables. Supports within-subject mediation analysis using 
    the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, 
    and extends Monte Carlo confidence interval estimation to missing data scenarios 
    using the 'semmcci' package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.

A weighted selection probability to locate rare variants associated with multiple phenotypes.

Implementation of Bayesian and frequentist Weibull Shape Parameter (WSP) tests for signal detection in pharmacovigilance based on right-censored time-to-event data to flag associations between drugs and adverse events. The WSP test is based on the assumption of constant hazard reflected by a Weibull type distribution with shape parameters equal to one. Based on the shape parameter estimates (posterior distribution or point estimate), the WSP test method performs a hypothesis test on each shape parameter and combines them to a decision on the presence of a signal. Methods described in Sauzet and Cornelius (2022) <doi:10.3389/fphar.2022.889088>, Sauzet et al. (2024) <doi:10.1007/s40264-024-01460-2>, and Dyck and Sauzet (2025) <doi:10.48550/arXiv.2412.05463>.