We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.

We implement various tests for the composite hypothesis of
    testing the fit to the family of inverse Gaussian distributions. 
    Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) <doi:10.48550/arXiv.1910.14119>, 
    as well as two tests from Henze and Klar (2002) <doi:10.1023/A:1022442506681>.
    Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) <doi:10.1016/j.jmva.2015.05.013>. 
    For each test a parametric bootstrap procedure is implemented.

Integrates with your 'RMarkdown' documents to automatically publish
  figures to the <https://GoFigr.io> service. Supports both 'knitr' and interactive 
  execution within 'RStudio'.

Tests of goodness-of-fit based on a kernel smoothing of the data.
    References:
       Pavía (2015) <doi:10.18637/jss.v066.c01>.

Goodness-of-fit tests for an arbitrary user-specified continuous
    distribution under Type-II right- or left-censoring. Implements the
    transformation-based method of Lin, Huang and Balakrishnan (2008)
    <doi:10.1109/TR.2008.2005860>, which uses a property of order statistics
    due to Malmquist (1950) to convert an r-out-of-n Type-II censored uniform
    sample into a complete sample of size r, alongside the earlier
    transformation of Michael and Schucany (1979)
    <doi:10.1080/00401706.1979.10489813>. Also implements the direct
    (untransformed) censored-sample statistics of Barr and Davidson (1973)
    <doi:10.1080/00401706.1973.10489108> and Pettitt and Stephens (1976)
    <doi:10.1093/biomet/63.2.291>, and the modified-statistic
    maximum-likelihood procedure of Chen and Balakrishnan (1995) for testing
    composite hypotheses. General background on empirical-distribution-function
    goodness-of-fit methods follows D'Agostino and Stephens (1986,
    ISBN:982-0-8247-7487-5).