The weighted ensemble method is a valuable approach for combining forecasts. This algorithm employs several optimization techniques to generate optimized weights. This package has been developed using algorithm of Armstrong (1989) <doi:10.1016/0024-6301(90)90317-W>.

A conditional independence test that can be applied both to
    univariate and multivariate random variables. The test is based on a
    weighted form of the sample covariance of the residuals after a
    nonlinear regression on the conditioning variables. Details are
    described in Scheidegger, Hoerrmann and Buehlmann (2022) "The Weighted
    Generalised Covariance Measure" <http://jmlr.org/papers/v23/21-1328.html>.
    The test is a generalisation of the Generalised Covariance Measure (GCM)
    implemented in the R package 'GeneralisedCovarianceMeasure' by Jonas Peters and
    Rajen D. Shah based on Shah and Peters (2020) "The Hardness of
    Conditional Independence Testing and the Generalised Covariance
    Measure" <doi:10.1214/19-AOS1857>.

An implementation of the Weighted Portmanteau Tests described
      in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing"
      published by the Journal of the American Statistical Association, Volume 107, 
      Issue 498, pages 777-787, 2012.

Performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2024) <doi:10.1080/01621459.2023.2221402>.  The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>.  The main functions are wgtRank(), wgtRankCI() and wgtRanktt().

Fast computation of
 Receiver Operating Characteristic (ROC) curves
 and Area Under the Curve (AUC)
 for weighted binary classification problems
 (weights are example-specific cost values).

The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011)  <doi:10.1093/biostatistics/kxr005>, Nikoloulopoulos (2016) <doi:10.1002/sim.6871> and Nikoloulopoulos (2017) <arXiv:1510.07376>.