Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.

Synthesizing joint distributions from marginal densities, focusing on controlling key statistical properties such as correlation for continuous data, mutual information for categorical data, and inducing Simpson's Paradox. Generate datasets with specified correlation structures for continuous variables, adjust mutual information between categorical variables, and manipulate subgroup correlations to intentionally create Simpson's Paradox.
  Joe (1997) <doi:10.1201/b13150>
  Sklar (1959) <https://en.wikipedia.org/wiki/Sklar%27s_theorem>.

Tests on properties of space-time covariance functions.
    Tests on symmetry, separability and for assessing 
    different forms of non-separability are available. Moreover tests on 
    some classes of covariance functions, such that the classes of 
    product-sum models, Gneiting models and integrated product models have 
    been provided.  It is the companion R package to the papers of  
    Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability 
    and some classes of space-time covariance function models <doi:10.1007/s00477-017-1472-2>
    and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for
    selecting a class of space-time covariance functions <doi:10.18637/jss.v094.i01>.

Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.

A compilation of tests for hypotheses regarding covariance
    and correlation matrices for one or more groups. The hypothesis can
    be specified through a corresponding hypothesis matrix and a vector or
    by choosing one of the basic hypotheses, while for the structure test,
    only the latter works. Thereby Monte-Carlo and Bootstrap-techniques
    are used, and the respective method must be chosen, and the functions
    provide p-values and mostly also estimators of calculated covariance
    matrices of test statistics. For more details on the methodology, see 
    Sattler et al. (2022) <doi:10.1016/j.jspi.2021.12.001>,
    Sattler and Pauly (2024) <doi:10.1007/s11749-023-00906-6>, and
    Sattler and Dobler (2025) <doi:10.48550/arXiv.2310.11799>.