Performs 18 omnibus tests yielding a total of 28 distinct methodological variations for testing the composite hypothesis of variance homogeneity.

An implementation of the Likelihood ratio Test (LRT) for testing that,
    in a (non)linear mixed effects model, the variances of a subset of the random
    effects are equal to zero. There is no restriction on the subset of variances
    that can be tested: for example, it is possible to test that all the variances
    are equal to zero. Note that the implemented test is asymptotic.
    This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'.
    Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.

Implements wild bootstrap tests for autocorrelation in Vector
    Autoregressive (VAR) models based on Ahlgren and Catani (2016)
    <doi:10.1007/s00362-016-0744-0>, a combined Lagrange Multiplier (LM)
    test for Autoregressive Conditional Heteroskedasticity (ARCH) in VAR
    models from Catani and Ahlgren (2016) <doi:10.1016/j.ecosta.2016.10.006>,
    and bootstrap-based methods for determining the cointegration rank from
    Cavaliere, Rahbek, and Taylor (2012) <doi:10.3982/ECTA9099> and
    Cavaliere, Rahbek, and Taylor (2014) <doi:10.1080/07474938.2013.825175>.

Implements a maximum likelihood estimation (MLE) method for
    estimation and prediction of Gaussian process-based spatially varying
    coefficient (SVC) models (Dambon et al. (2021a)
    <doi:10.1016/j.spasta.2020.100470>).  Covariance tapering (Furrer et
    al. (2006) <doi:10.1198/106186006X132178>) can be applied such that
    the method scales to large data. Further, it implements a joint
    variable selection of the fixed and random effects (Dambon et al.
    (2021b) <doi:10.1080/13658816.2022.2097684>). The package and its 
    capabilities are described in (Dambon et al. (2021c)
    <doi:10.48550/arXiv.2106.02364>).

Provide a collection of miscellaneous R functions
    related to the Vasicek distribution with the intent to make
    the lives of risk modelers easier.