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>.

Provides statistical methods to check if a parametric family of
  conditional density functions fits to some given dataset of covariates and 
  response variables. Different test statistics can be used to determine the 
  goodness-of-fit of the assumed model, see Andrews (1997) 
  <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>,
  Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta 
  (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the 
  corresponding p-values are approximated using a parametric bootstrap method.

An interactive document on  the topic of goodness of fit analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.

Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, 'Weibull', 'Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.

Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
	     for continuous univariate distributions, using
	     efficient algorithms.