Density, distribution, quantile and hazard functions of a
    stable variate; generalized regression models for the parameters
    of a stable distribution. See the README for how to make equivalent calls
    to those of 'stabledist' (i.e., Nolan's 0-parameterization and 
    1-parameterization as detailed in Nolan (2020)). 
    See github for Lambert and Lindsey 1999 JRSS-C journal article, 
    which details the parameterization of the Buck (1995) stable.   
    See the Details section of the `?dstable` help file for context and 
    references.

Density, Probability and Quantile functions, and random number
  generation for (skew) stable distributions, using the parametrizations of
  Nolan.

Estimate the four parameters of stable laws using maximum
             likelihood method, generalised method of moments with
             finite and continuum number of points, iterative
             Koutrouvelis regression and Kogon-McCulloch method.  The
             asymptotic properties of the estimators (covariance
             matrix, confidence intervals) are also provided.

Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough   to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected  and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and  Knudson (2018) <arXiv:1812:09384>.

Graphical and computational methods that can be used to assess the 
  stability of results from supervised statistical learning.