Variable selection from random forests using both
        backwards variable elimination (for the selection of small sets
        of non-redundant variables) and selection based on the
        importance spectrum (somewhat similar to scree plots; for the
        selection of large, potentially highly-correlated variables).
        Main applications in high-dimensional data (e.g., microarray
        data, and other genomics and proteomics applications).

Vector autoregressive (VAR) model is a fundamental and effective approach
    for multivariate time series analysis. Shrinkage estimation methods can be
    applied to high-dimensional VAR models with dimensionality greater than
    the number of observations, contrary to the standard ordinary least squares
    method. This package is an integrative package delivering nonparametric,
    parametric, and semiparametric methods in a unified and consistent manner,
    such as the multivariate ridge regression in Golub, Heath, and Wahba (1979)
    <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in
    Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and
    Bayesian estimation methods using noninformative and informative priors
    in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and
    Ni and Sun (2005) <doi:10.1198/073500104000000622>.

Performs 20 omnibus tests 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>.