Five omnibus tests for testing the composite hypothesis of
        normality.

Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.

Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and  Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for  stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.

Calculate NOS (node overlap and segregation) and
    the associated metrics described in Strona and Veech (2015)
    <doi:10.1111/2041-210X.12395> and Strona et al. (2018) 
    <doi:10.1111/ecog.03447>. The functions provided in the 
    package enable assessment of structural patterns ranging from
    complete node segregation to perfect nestedness in a variety 
    of network types. In addition, they provide a measure of 
    network modularity.

Provides functions for classifying sparseness in 2 x 2 categorical data where one or more cells have zero counts. The classification uses three widely applied summary measures: Risk Difference (RD), Relative Risk (RR), and Odds Ratio (OR). Helps in selecting suitable continuity corrections for zero cells in multi-centre or meta-analysis studies. Also supports sensitivity analysis and can detect phenomena such as Simpson's paradox. The methodology is based on Subbiah and Srinivasan (2008) <doi:10.1016/j.spl.2008.06.023>.

Provides a cross-platform interface to prevent the operating system from going to sleep while long-running R tasks are executing.