Runs resampling-based tests jointly, e.g., sign-flip score tests from Hemerik et al., (2020) <doi:10.1111/rssb.12369>, to allow for multivariate testing, i.e., weak and strong control of the Familywise Error Rate or True Discovery Proportion.

Implementation of a parametric joint model for modelling recurrent
  and competing event processes using generalised survival models as described in
  Entrop et al., (2025) <doi:10.1002/bimj.70038>. The joint  model can 
  subsequently be used to predict the mean number of events in the
  presence of competing risks at different time points. Comparisons of the mean
  number of event functions, e.g. the differences in mean number of events
  between two exposure groups, are also available.

Jointly estimates two-group means and covariances
    for matrix-variate data and calculates test statistics.  
    This package implements the algorithms defined in 
    Hornstein, Fan, Shedden, and Zhou (2018) 
    <doi:10.1080/01621459.2018.1429275>.

Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.

Estimate risk caused by two extreme and dependent forcing variables using bivariate extreme value models as described in Zheng, Westra, and Sisson (2013) <doi:10.1016/j.jhydrol.2013.09.054>; Zheng, Westra and Leonard (2014) <doi:10.1002/2013WR014616>; Zheng, Leonard and Westra (2015) <doi:10.2166/hydro.2015.052>.

Methods for fast segmentation of multivariate
    signals into piecewise constant profiles and for generating realistic
    copy-number profiles. A typical application is the joint segmentation of total
    DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism
    (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, 
    Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.

In the observational study design stage, matching/weighting methods are
    conducted. However, when many background variables are present, the decision as to 
    which variables to prioritize for matching/weighting is not trivial. Thus, the 
    joint treatment-outcome variable importance plots are created to guide variable
    selection. The joint variable importance plots enhance variable comparisons via
    unadjusted bias curves derived under the omitted variable bias framework. The 
    plots translate variable importance into recommended values for tuning parameters 
    in existing methods. Post-matching and/or weighting plots can also be used to 
    visualize and assess the quality of the observational study design. The method 
    motivation and derivation is presented in "Prioritizing Variables for Observational 
    Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) 
    <doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel 
    (2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.