Performs Wilcoxon-Mann-Whitney test in the presence of missing data with controlled Type I error regardless of the values of missing data.

Power calculator for the two-sample Wilcoxon-Mann-Whitney rank-sum test for a continuous outcome (Mollan, Trumble, Reifeis et. al., Mar. 2020) <doi:10.1080/10543406.2020.1730866> <arXiv:1901.04597>, (Mann and Whitney 1947) <doi:10.1214/aoms/1177730491>, (Shieh, Jan, and Randles 2006) <doi:10.1080/10485250500473099>.

This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.

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.

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>.