Implementing seven Covariate-Adaptive Randomization to assign patients to two treatments. 
    Three of these procedures can also accommodate quantitative and mixed covariates. Given a set of covariates, the user can
    generate a single sequence of allocations or replicate the design multiple times by simulating the patients' covariate
    profiles. At the end, an extensive assessment of the performance of the randomization procedures is provided, calculating
    several imbalance measures. See Baldi Antognini A, Frieri R, Zagoraiou M and Novelli M (2022) <doi:10.1007/s00362-022-01381-1> for details.

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>.