In the case of multivariate ordinal responses, parameter estimates can be severely biased if personal response styles are ignored. This packages provides methods to account for personal response styles and to explain the effects of covariates on the response style, as proposed by Schauberger and Tutz 2021 <doi:10.1177/1471082X20978034>. The method is implemented both for the multivariate cumulative model and the multivariate adjacent categories model.

Dichotomous responses having two categories can be analyzed
    with stats::glm() or lme4::glmer() using the family=binomial option.
    Unfortunately, polytomous responses with three or more unordered
    categories cannot be analyzed similarly because there is no analogous
    family=multinomial option. For between-subjects data,
    nnet::multinom() can address this need, but it cannot handle random
    factors and therefore cannot handle repeated measures. To address this
    gap, we transform nominal response data into counts for each categorical 
    alternative. These counts are then analyzed using (mixed) Poisson regression
    as per Baker (1994) <doi:10.2307/2348134>. Omnibus analyses of variance can be 
    run along with post hoc pairwise comparisons. For users wishing to analyze nominal 
    responses from surveys or experiments, the functions in this package essentially 
    act as though stats::glm() or lme4::glmer() provide a family=multinomial option.

Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) <http://www.jstor.org/stable/1392277> proposal.

Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).

Multivariate generalized Gaussian distribution,
              Multivariate Cauchy distribution,
              Multivariate t distribution.
              Distance between two distributions (see N. Bouhlel and A. Dziri (2019): <doi:10.1109/LSP.2019.2915000>,
              N. Bouhlel and D. Rousseau (2022): <doi:10.3390/e24060838>,
              N. Bouhlel and D. Rousseau (2023): <doi:10.1109/LSP.2023.3324594>).
              Manipulation of these multivariate probability distributions.