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.

The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.