Title: Estimate Functional and Stochastic Parameters of Linear Models
person("Davide Antonio", "Cucci", role = c("aut")),
person("Lionel", "Voirol", email = "lionelvoirol@hotmail.com", role = c("aut", "cre")),
person("Stéphane", "Guerrier", role = "aut"),
person("Jean-Philippe", "Montillet", role = "ctb"),
person("Gaël", "Kermarrec", role = "ctb")
Description: Implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2022) <arXiv:2206.09668>.
The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals.
The 'gmwmx' package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.
Imports: Rcpp, fs, stringi, wv, Matrix, longmemo, rjson

Imports: Rcpp, fs, stringi, wv, Matrix, longmemo, rjson, ltsa

Title: Goodness of Fit Test for Continuous Distribution Functions
Description: Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions.

Description: Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures.