Implements the distribution-free goodness-of-fit regression testing
  procedure, introduced by Estate Khmaladze (2021, <doi:10.1007/s10463-021-00786-3>)
  to test whether or not the mean structure of a parametric model belongs to a
  specified model family. The test is implemented for general mean functions
  with minimal distributional assumptions as well as common models (e.g., lm,
  glm) with the usual model assumptions.

Given a set of predictive quantiles from a distribution,
    estimate the distribution and create `d`, `p`, `q`, and `r` functions
    to evaluate its density function, distribution function, and quantile
    function, and generate random samples. On the interior of the provided
    quantiles, an interpolation method such as a monotonic cubic spline is
    used; the tails are approximated by a location-scale family.

Scientific and technical article format for the web.
    'Distill' articles feature attractive, reader-friendly typography,
    flexible layout options for visualizations, and full support for
    footnotes and citations.

Interface to 'DistillerSR' APIs. See
  <https://apidocs.evidencepartners.com/> for more details.

Some very simple method functions for confidence interval calculation, bootstrap resampling aimed at atmospheric science applications, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.  To reference this package and for a tutorial on the bootstrap functions, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0069.1> and Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.

Calculates Distinctiveness Centrality in social networks. 
	For formulas and descriptions, see Fronzetti Colladon and Naldi (2020) <doi:10.1371/journal.pone.0233276>.

Create and evaluate probability distribution objects from a
    variety of families or define custom distributions. Automatically compute
    distributional properties, even when they have not been specified.
    This package supports statistical modeling and simulations, and forms
    the core of the probaverse suite of R packages.