weakARMA
1.0.3Tools for the Analysis of Weak ARMA Models
Overview
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.
Install
Health
- OK2026-08-0413 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 93%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 52%
Downloads
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Code & Tests
Datasets
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.0.3Latest
- 1.0.22022-02-07
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2022-02-07
- Total releases
- 2 / 4 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.4.1
- Bundled data
- 144 KB / 3 files
- Download size
- 171 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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