Provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection,  regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) <DOI:10.1002/sim.6235>.

Applies affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.

Automates set operations (i.e., comparisons of overlap) between multiple vectors.
    It also contains a function for automating reporting in 'RMarkdown', by generating markdown output for easy analysis, as well as an 'RMarkdown' template for use with 'RStudio'.

Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.

Provides functions for estimation (parametric, semi-parametric and non-parametric)
             of copula-based dependence coefficients between a finite collection of random vectors,
             including phi-dependence measures and Bures-Wasserstein dependence measures. 
             An algorithm for agglomerative hierarchical variable clustering is also implemented.
             Following the articles De Keyser & Gijbels (2024) <doi:10.1016/j.jmva.2024.105336>,
             De Keyser & Gijbels (2024) <doi:10.1016/j.ijar.2023.109090>, and De Keyser & Gijbels (2024)
             <doi:10.48550/arXiv.2404.07141>.