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.

Tools for fitting statistical network models to dynamic network data.
   Can be used for fitting both dynamic network actor models ('DyNAMs') and
   relational event models ('REMs').
   Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>,
   Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>,
   Stadtfeld and Block (2017) <doi:10.15195/v4.a14>,
   Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.

An opinionated framework for building a production-ready
    'Shiny' application. This package contains a series of tools for
    building a robust 'Shiny' application from start to finish.

A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.

Allows the user to animate text within 'rmarkdown' documents and 'shiny' applications. 
    The animations are activated using the 'Animate.css' library. See <https://animate.style/> for more information.

Estimates hazard ratios and mortality differentials for
    doubly-truncated data without population denominators. This method is
    described in Goldstein et al. (2023) <doi:10.1007/s11113-023-09785-z>.