Implements a generalized goodness-of-fit test based on spacings 
    for general progressive Type-II censored data. The test statistic is based 
    on the work of Qin et al. (2022) <doi:10.1080/02664763.2020.1821613> and 
    extends the methodology of Balakrishnan et al. (2003) 
    <doi:10.1007/978-1-4612-0103-8_8>. Users can test data against any 
    distribution by providing custom pdf, cdf, and survival functions. The 
    package supports both normal approximation and Monte Carlo simulation 
    approaches for computing p-values and critical values.

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.

Provision of classes and methods for estimating generalized
   orthogonal GARCH models. This is an alternative approach to CC-GARCH models
   in the context of multivariate volatility modeling.