A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by
  easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density,
  Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)
  <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of
  the loglikelihood and a random generator for Davidian curves.

Diagnostic and prognostic models are typically evaluated with
    measures of accuracy that do not address clinical consequences.
    Decision-analytic techniques allow assessment of clinical outcomes,
    but often require collection of additional information may be
    cumbersome to apply to models that yield a continuous result. Decision
    curve analysis is a method for evaluating and comparing prediction
    models that incorporates clinical consequences, requires only the data
    set on which the models are tested, and can be applied to models that
    have either continuous or dichotomous results. See the following references 
    for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>,
    Vickers (2008) <doi:10.1186/1472-6947-8-53>, 
    and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.

Fits Bayesian copula vector autoregressive models for bivariate
    time series with dynamic, regime-switching, and constant dependence
    structures. The package includes simulation, data preparation,
    estimation with 'Stan' through 'rstan' or 'cmdstanr', posterior
    summaries, diagnostics, trajectory extraction, fitted and predictive
    summaries, and approximate leave-one-out cross-validation model
    comparison for supported fits. For Bayesian computation and model
    comparison, see Carpenter et al. (2017)
    <doi:10.18637/jss.v076.i01> and Vehtari, Gelman and Gabry (2017)
    <doi:10.1007/s11222-016-9696-4>.

A collection of tests to analyze the causal direction of dependence in linear models (Wiedermann, W., & von Eye, A., 2025, ISBN: 9781009381390). The package includes functions to perform Direction Dependence Analysis for variable distributions, residual distributions, and independence properties of predictors and residuals in competing causal models. In addition, the package contains functions to test the causal direction of dependence in conditional models (i.e., models with interaction terms) For more information see <https://www.ddaproject.com>.