Flexible implementation of a structural change point detection algorithm for multivariate time series.
    It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system.
    Bai, Lumsdaine, and Stock (1998) <doi:10.1111/1467-937X.00051>.

Evaluate hypotheses concerning the distribution of multinomial
  proportions using bridge sampling. The bridge sampling routine is able to
  compute Bayes factors for hypotheses that entail inequality constraints,
  equality constraints, free parameters, and mixtures of all three. These
  hypotheses are tested against the encompassing hypothesis, that all parameters
  vary freely or against the null hypothesis that all category proportions are equal.
  For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.

Implements a generalization of the Cochran-Armitage trend test to
    multinomial data. In addition to an overall test, multiple testing adjusted
    p-values for trend in individual outcomes and power calculation is
    available.

Provides a basic interface for accessing annotation data from
   the Multi-CAST collection, a database of spoken natural language texts
   edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
   diverse set of languages and has been annotated across multiple levels.
   Annotation data is downloaded on request from the servers of the
   University of Bamberg. See the Multi-CAST website
   <https://multicast.aspra.uni-bamberg.de/> for more information and a list
   of related publications.

Implements methods for multiple canonical correlation analysis (CCA) for more than two data blocks, 
    with a focus on multivariate repeated measures and functional data. The package provides two approaches: 
    (i) multiple kernel CCA, which embeds each data block into a reproducing kernel Hilbert space to capture 
    nonlinear dependencies, and (ii) multiple functional CCA, which represents repeated measurements as smooth 
    functions and performs analysis in a Hilbert space framework. Both approaches are formulated via covariance 
    operators and solved as generalized eigenvalue problems with regularization to ensure numerical stability. 
    The methods allow estimation of canonical variables, generalized canonical correlations, and low-dimensional 
    representations for exploratory analysis and visualization of dependence structures across multiple feature sets. 
    The implementation follows the framework developed in Górecki, Krzyśko, Gnettner and Kokoszka (2025) 
    <doi:10.48550/arXiv.2510.04457>.