Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2022) <arXiv:2203.04619>. It provides autoregressive moving average correlation structures and binary, ordinal, Poisson, and negative binomial regressions.

Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting (fuzzy) clusters of state sequences. Parametric bootstraps methods to validate typology of sequences are also provided.

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 (2021) "The Weighted
    Generalised Covariance Measure" <arXiv:2111.04361>. 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" <arXiv:1804.07203>.

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 (2018) <doi:10.1214/18-AOAS1153>.  The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>.  The main functions are wgtRank() and wgtRanktt() and ef2C().