Numerous time series admit autoregressive moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent. These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments 'p', 'q', 'ar' and 'ma'. See Francq, C. and Zakoïan, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and Boubacar Maïnassara, Y. and Saussereau, B. (2018) <doi:10.1080/01621459.2017.1380030> for more details.
Version: 1.0.3 Depends: R (≥ 3.4.1) Imports: CompQuadForm (≥ 1.4.3), MASS (≥ 7.3-54), matrixStats (≥ 0.61), vars (≥ 1.5-6) Suggests: timeSeries, testthat, knitr, rmarkdown, renv Published: 2022-04-04 DOI: 10.32614/CRAN.package.weakARMA Author: Yacouba Boubacar Maïnassara [aut], Julien Yves Rolland [aut, cre], Coraline Parguey [ctb], Vincent Mouillot [ctb] Maintainer: Julien Yves Rolland <julien.rolland at univ-fcomte.fr> BugReports: https://plmlab.math.cnrs.fr/jrolland/weakARMA/-/issues License: GPL (≥ 3) URL: https://plmlab.math.cnrs.fr/jrolland/weakARMA NeedsCompilation: no Materials: README CRAN checks: weakARMA results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=weakARMA to link to this page.
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