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>.

Consider a goodness-of-fit (GOF) problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze martingale transformation method - which was proposed by Khmaladze (1981) <DOI:10.1137/1126027> - provides asymptotic distribution free test for the GOF problem. This package contains one function: KhmaladzeTrans(). In this version, KhmaladzeTrans() provides test statistic and critical value of GOF test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) <DOI:10.1007/s00180-020-00971-7> and tests for other distributions will be available at the later version.

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.