Generalized Method of Wavelet Moments (GMWM) is an estimation
technique for the parameters of time series models. It uses the wavelet variance
in a moment matching approach that makes it particularly suitable for the
estimation of certain state-space models. Furthermore, there exists a robust
implementation of GMWM, which allows the robust estimation of some state-space
models and ARIMA models. Lastly, the package provides the ability to quickly
generate time series data, perform different wavelet decompositions, and
visualizations.
Rcpp (>= 0.12.2), scales, grid, reshape2, utils, stats,
methods, graphics, grDevices

Estimate Functional and Stochastic Parameters of Linear Models
with Correlated Residuals
c(
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")
)
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.
Rcpp, fs, stringi, wv, Matrix, longmemo, rjson
Davide Antonio Cucci [aut],
Lionel Voirol [aut, cre],
Stéphane Guerrier [aut],
Jean-Philippe Montillet [ctb],
Gaël Kermarrec [ctb]

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

Goodness of Fit Test for Continuous Distribution Functions
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.