weakARMA: Tools for the Analysis of Weak ARMA Models
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:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.