New tools for the imputation of missing values in high-dimensional
    data are introduced using the non-parametric nearest neighbor methods. It
    includes weighted nearest neighbor imputation methods that use specific
    distances for selected variables. It includes an automatic procedure of cross
    validation and does not require prespecified values of the tuning parameters.
    It can be used to impute missing values in high-dimensional data when the sample
    size is smaller than the number of predictors. For more information see Faisal
    and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.

Estimates Poole and Rosenthal's (1985 <doi:10.2307/2111172>, 1991 <doi:10.2307/2111445>)
	W-NOMINATE scores from roll call votes supplied though a 'rollcall'
	object from the 'pscl' package.

Provides regression modeling and prediction for the marginal
    mean of recurrent events in the presence of a competing terminal event
    using the weighted nonparametric maximum likelihood estimator (wNPMLE)
    of Bellach and Kosorok (2026)
    <doi:10.48550/arXiv.2605.25934>. Two classes of transformation
    models are implemented: Box-Cox transformation models and logarithmic
    transformation models. These extend the proportional means model of
    Ghosh and Lin (2002) <doi:10.17615/pt0g-y207> and the transformation
    model framework of Zeng and Lin (2006)
    <doi:10.1093/biomet/93.3.627>. Parameter estimation is performed using
    automatic differentiation through the Template Model Builder (TMB)
    framework. Standard errors are computed using sandwich variance
    estimators that account for estimation of the inverse-probability
    censoring weights following Bellach, Kosorok, Rüschendorf and Fine
    (2019) <doi:10.1080/01621459.2017.1401540>.

Implements the Whale Optimization Algorithm(WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. The methodology is based on "The Whale Optimization Algorithm" by Mirjalili and Lewis (2016) <doi:10.1016/j.advengsoft.2016.01.008>.