Provides an extension to the 'purrr' family of mapping
    functions to apply a function to each combination of elements in a
    list of inputs.  Also includes functions for automatically detecting
    output type in mapping functions, finding every combination of
    elements of lists or rows of data frames, and applying multiple models
    to multiple subsets of a dataset.

Performs the cross-match test that is an exact, 
		distribution free test of equality of 2 high dimensional multivariate distributions.   
		The input is a distance matrix and the labels of
        the two groups to be compared, the output is the number of
        cross-matches and a p-value. See Rosenbaum (2005) <doi:10.1111/j.1467-9868.2005.00513.x>.

Network meta-analysis and meta-regression (allows
  including up to three covariates) for individual participant data,
  aggregate data, and mixtures of both formats using the three-level
  hierarchical model. Each format can come from randomized controlled
  trials or non-randomized studies or mixtures of both. Estimates are
  generated in a Bayesian framework using JAGS. The implemented models
  are described by Hamza et al. 2023 <DOI:10.1002/jrsm.1619>.

Generate and analyse crossover designs from combinatorial or search algorithms as well as from literature and a GUI to
    access them.

Joint distribution of number of crossings and the 
  longest run in a series of independent Bernoulli trials. The
  computations uses an iterative procedure where computations 
  are based on results from shorter series. The procedure 
  conditions on the start value and partitions by further 
  conditioning on the position of the first crossing (or none).

Create descriptive tables for continuous and categorical variables. 
    Apply summary statistics and counting function, with or without a grouping variable, and create beautiful reports using 'rmarkdown' or 'officer'.
    You can also compute effect sizes and statistical tests if needed.

Provides 'SPSS'- and 'SAS'-like output for cross tabulations of two
  categorical variables (CROSSTABS) and for hierarchical loglinear analyses 
  of two or more categorical variables (LOGLINEAR). The methods are described in
  Agresti (2013, ISBN:978-0-470-46363-5),
  Ajzen & Walker (2021, ISBN:9780429330308),
  Field (2018, ISBN:9781526440273),
  Norusis (2012, ISBN:978-0-321-74843-0),
  Nussbaum (2015, ISBN:978-1-84872-603-1),
  Stevens (2009, ISBN:978-0-8058-5903-4),
  Tabachnik & Fidell (2019, ISBN:9780134790541), and
  von Eye & Mun (2013, ISBN:978-1-118-14640-8).