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iAR

Description

Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive (iAR) model Eyheramendy et al.(2018), the complex irregular autoregressive (CiAR) model Elorrieta et al.(2019) and the bivariate irregular autoregressive (BiAR) model Elorrieta et al.(2021).

Installation

You can install the released version of iAR from Github with:

remotes::install_github("felipeelorrieta/iAR-package")

Code of Conduct

Please note that the iAR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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Version

Install

install.packages('iAR')

Monthly Downloads

279

Version

1.3.2

License

GPL-2

Maintainer

Elorrieta Felipe

Last Published

October 24th, 2025

Functions in iAR (1.3.2)

phase

Computing phased time series
plot_fit

Plot Fit Method for unidata Objects
plot

Plot Method for unidata Objects
plot_forecast

Plot Forecast Method for unidata Objects
pairingits

Pairing two irregularly observed time series
summary

Summary Method for unidata Objects
utilities

Utilities Class
unidata

Unidata Class
sim

Simulate Time Series for Multiple iAR Models
clcep

Classical Cepheid
BiAR

`BiAR` Class
CiAR

`CiAR` Class
interpolation

Interpolation for iAR, CiAR, and BiAR Classes
iAR

`iAR` Class
gentime

Generating Irregularly spaced times
multidata

Multidata Class
loglik

Maximum Likelihood Estimation for iAR Models
iAR-package

iAR: Irregularly Observed Autoregressive Models
fit

Fitted Values for iAR, CiAR, and BiAR Classes
forecast

Forecast for iAR, CiAR, and BiAR Classes
harmonicfit

Harmonic Fit to Time Series
eb

Eclipsing Binaries (Beta Lyrae)
Planets

Transit of an extrasolar planet
cvnovag

ZTF g-band Cataclysmic Variable/Nova
dmcep

Double Mode Cepheid.
cvnovar

ZTF r-band Cataclysmic Variable/Nova
agn

Active Galactic Nuclei
dscut

Delta Scuti
kalman

Maximum Likelihood Estimation of Parameters for iAR, CiAR, and BiAR Models using the Kalman Filter