Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.

Performs adjusted inferences based on model objects fitted, using 
    maximum likelihood estimation, by the extreme value analysis packages
    'eva' <https://cran.r-project.org/package=eva>, 
    'evd' <https://cran.r-project.org/package=evd>, 
    'evir' <https://cran.r-project.org/package=evir>, 
    'extRemes' <https://cran.r-project.org/package=extRemes>, 
    'fExtremes' <https://cran.r-project.org/package=fExtremes>, 
    'ismev' <https://cran.r-project.org/package=ismev>, 
    'mev' <https://cran.r-project.org/package=mev>, 
    'POT' <https://cran.r-project.org/package=POT> and
    'texmex' <https://cran.r-project.org/package=texmex>. 
    Adjusted standard errors and an adjusted loglikelihood are provided, using    
    the 'chandwich' package <https://cran.r-project.org/package=chandwich>
    and the object-oriented features of the 'sandwich' package 
    <https://cran.r-project.org/package=sandwich>. The adjustment is based on a 
    robust sandwich estimator of the parameter covariance matrix, based on the 
    methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This 
    can be used for cluster correlated data when interest lies in the 
    parameters of the marginal distributions, or for performing inferences that 
    are robust to certain types of model misspecification.  Univariate extreme 
    value models, including regression models, are supported.

Creating efficiently new column(s) in a data frame (including tibble) by applying a function one row at a time.