iAR: Irregularly Observed Autoregressive Models
Data sets and functions for implementing discrete-time autoregressive models for irregularly observed time series. The models implemented include the irregular autoregressive (iAR) model (Eyheramendy et al. (2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive (CiAR) model (Elorrieta et al. (2019) <doi:10.1051/0004-6361/201935560>), and the bivariate irregular autoregressive (BiAR) model (Elorrieta et al. (2021) <doi:10.1093/mnras/stab1216>). For a comprehensive description of the package and its applications, see Elorrieta et al. (2026) <doi:10.32614/RJ-2026-036>.
| Version: |
1.3.5 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp (≥ 1.0.7), ggplot2, stats, Rdpack, S7, zoo |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
arfima, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-10-27 |
| DOI: |
10.32614/CRAN.package.iAR |
| Author: |
Elorrieta Felipe [aut, cre],
Ojeda Cesar [aut],
Eyheramendy Susana [aut],
Palma Wilfredo [aut] |
| Maintainer: |
Elorrieta Felipe <felipe.elorrieta at usach.cl> |
| License: |
GPL-2 |
| URL: |
https://felipeelorrieta.github.io/iAR-package/ |
| NeedsCompilation: |
yes |
| Citation: |
iAR citation info |
| Materials: |
README |
| CRAN checks: |
iAR results |
Documentation:
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