A post-estimation method for categorical response models (CRM). 
    Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), 
    vglm() and glm() are currently supported. Available tests include the 
    Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic 
    regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal 
    models. The proportional odds, adjacent-category, and constrained continuation-ratio 
    models are particularly supported at ordinal level. Tests for the proportional 
    odds assumptions in ordinal models are also possible with the Brant and the 
    Likelihood-Ratio tests. Moreover, several summary measures of predictive strength 
    (Pseudo R-squared), and some useful error metrics, including, the brier 
    score, misclassification rate and logloss are also available for the 
    binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) 
    <http://www.statmod.org/workshops_archive_proceedings_2018.html>.

Graphical tools and goodness-of-fit tests for complete and right-censored data:
             1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests,
                which use the empirical distribution function for complete data 
                and are extended for right-censored data.
             2. Generalized chi-squared-type test, which is based on the squared 
                differences between observed and expected counts using random 
                cells with right-censored data.
             3. A series of graphical tools such as probability or cumulative hazard 
                plots to guide the decision about the most suitable parametric model 
                for the data.

Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.