Nothing
#' LibstableR: Fast and accurate evaluation, random number generation
#' and parameter estimation of skew stable distributions.
#'
#' LibstableR provides functions to work with skew stable distributions
#' in a fast and accurate way \[1]. It performs:
#'
#' * Fast and accurate evaluation of the probability density function (PDF) and cumulative density function (CDF).
#' * Fast and accurate evaluation of the quantile function (CDF^{-1}).
#' * Random numbers generation \[2].
#' * Skew stable parameter estimation with:
#' * McCulloch's method of quantiles \[3].
#' * Koutrouvellis' method based on the characteristic function \[4].
#' * Maximum likelihood estimation.
#' * Modified maximum likelihood estimation as described in \[1].
#' *The evaluation of the PDF and CDF is based on the formulas provided by John P Nolan in \[5].
#'
#' @md
#' @author Javier Royuela del Val, Federico Simmross Wattenberg and Carlos Alberola López;\cr\cr
#' Maintainer: Javier Royuela del Val <jroyval@@lpi.tel.uva.es>
#' @references
#' * \[1] Royuela-del-Val J, Simmross-Wattenberg F, Alberola López C (2017). libstable: Fast, Parallel and High-Precision Computation of alpha-stable Distributions in R, C/C++ and MATLAB. Journal of Statistical Software, 78(1), 1-25. doi:10.18637/jss.v078.i01
#' * \[2] Chambers JM, Mallows CL, Stuck BW (1976). A Method for Simulating Stable Random Variables. Journal of the American Statistical Association, 71(354), 340-344. doi:10.1080/01621459.1976.10480344
#' * \[3] McCulloch JH (1986). Simple Consistent Estimators of Stable Distribution Parameters. Communications in Statistics - Simulation and Computation, 15(4), 1109-1136. doi:10.1080/03610918608812563
#' * \[4] Koutrouvelis IA (1981). An Iterative Procedure for the Estimation of the Parameters of Stable Laws. Communications in Statistics - Simulation and Computation, 10(1), 17-28. doi:10.1080/03610918108812189
#' * \[5] Nolan JP (1997). Numerical Calculation of Stable Densities and Distribution Functions. Stochastic Models, 13(4), 759-774. doi:10.1080/15326349708807450
#' @name libstableR
#' @docType package
#' @keywords package
#' @useDynLib libstableR, .registration=TRUE
#' @importFrom Rcpp sourceCpp evalCpp
#' @examples
#' # Set alpha, beta, sigma and mu stable parameters in a vector
#' pars <- c(1.5, 0.9, 1, 0)
#'
#' # Generate an abscissas axis and probabilities vector
#' x <- seq(-5, 10, 0.05)
#' p <- seq(0.01, 0.99, 0.01)
#'
#' # Calculate pdf, cdf and quantiles
#' pdf <- stable_pdf(x, pars)
#' cdf <- stable_cdf(x, pars)
#' xq <- stable_q(p, pars)
#'
#' # Generate 300 random values
#' rnd <- stable_rnd(300, pars)
#'
#' # Estimate the parameters of the skew stable distribution given
#' # the generated sample:
#'
#' # Using the McCulloch's estimator:
#' pars_est_M <- stable_fit_init(rnd)
#'
#' # Using the Koutrouvelis' estimator:
#' pars_est_K <- stable_fit_koutrouvelis(rnd, pars_est_M)
#'
#' # Using maximum likelihood estimator, with McCulloch estimation
#' # as a starting point:
#' # pars_est_ML <- stable_fit_mle(rnd, pars_est_M)
#'
#' # Using modified maximum likelihood estimator (See [1]):
#' # pars_est_ML2 <- stable_fit_mle2d(rnd, pars_est_M)
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.