% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/01_BiARclass.R
\name{BiAR}
\alias{BiAR}
\title{`BiAR` Class}
\usage{
BiAR(
  times = integer(0),
  series = integer(0),
  series_esd = integer(0),
  series_names = character(0),
  fitted_values = integer(0),
  loglik = integer(0),
  kalmanlik = integer(0),
  coef = c(0.8, 0),
  tAhead = 1,
  rho = 0,
  forecast = integer(0),
  interpolated_values = integer(0),
  interpolated_times = integer(0),
  interpolated_series = integer(0),
  zero_mean = TRUE,
  standardized = TRUE,
  hessian = FALSE,
  summary = list()
)
}
\arguments{
\item{times}{A numeric vector representing the time points.}

\item{series}{A numeric matrix or vector representing the values of the time series.}

\item{series_esd}{A numeric matrix or vector representing the error standard deviations of the time series.}

\item{series_names}{An optional character vector representing the name of the series.}

\item{fitted_values}{A numeric vector containing the fitted values from the model.}

\item{loglik}{A numeric value representing the log-likelihood of the model.}

\item{kalmanlik}{A numeric value representing the Kalman likelihood of the model.}

\item{coef}{A numeric vector of length 2 containing the autoregressive (phiR) and
cross-correlation (phiI) coefficients of the model. Each value must be in [-1, 1]
and the modulus `sqrt(phiR^2 + phiI^2)` must be less than 1. Defaults to `c(0.8, 0)`.
Unlike [iAR], which uses a scalar, both [CiAR] and `BiAR` use a length-2 vector.}

\item{tAhead}{A numeric value specifying the forecast horizon (default: 1).}

\item{rho}{A numeric vector containing the estimated coefficients of the model.}

\item{forecast}{A numeric vector containing the forecasted values.}

\item{interpolated_values}{A numeric vector containing the interpolated values.}

\item{interpolated_times}{A numeric vector containing the times of the interpolated data points.}

\item{interpolated_series}{A numeric vector containing the interpolated series.}

\item{zero_mean}{A logical value indicating if the model assumes a zero-mean process (default: TRUE).}

\item{standardized}{A logical value indicating if the model assumes a standardized process (default: TRUE).}

\item{hessian}{A logical value indicating whether the Hessian matrix is computed during estimation (default: FALSE).}

\item{summary}{A list containing the summary of the model fit, including diagnostics and statistical results.}
}
\description{
Represents a bivariate irregular autoregressive (BiAR) time series model.
This class extends the `multidata` class and provides additional properties
for modeling, forecasting, and interpolation of bivariate time series data.
}
\details{
The `BiAR` class is designed to handle bivariate irregularly observed time series data
using an autoregressive approach. It extends the `multidata` class to include 
additional properties for modeling bivariate time series.

Key features of the `BiAR` class include:
- Support for bivariate time series data.
- Forecasting and interpolation functionalities for irregular time points.
- Assumptions of zero-mean and standardized processes, configurable by the user.
- Estimation of model parameters and likelihoods, including Kalman likelihood.
}
\section{Validation Rules}{

- `@times` must be a numeric vector without dimensions and strictly increasing.
- `@series` must be a numeric matrix with two columns (bivariate) or be empty.
- The number of rows in `@series` must match the length of `@times`.
- `@series_esd`, if provided, must be a numeric matrix. Its dimensions must match those of `@series`, or it must have one row and the same number of columns.
- If `@series_esd` contains NA values, they must correspond positionally to NA values in `@series`.
- `@series_names`, if provided, must be a character vector with length equal to the number of columns in `@series`, and all names must be unique.
- `@coef` must be a numeric vector of length 2, with each element strictly between -1 and 1.
- The modulus `sqrt(phiR^2 + phiI^2)` must be strictly less than 1 (stationarity condition).
- `@tAhead` must be a strictly positive numeric scalar.
}

\examples{
times=gentime(n=200, distribution = "expmixture",
             lambda1 = 130, lambda2 = 6.5, p1 = 0.15, p2 = 0.85)@times
my_BiAR <- BiAR(times = times,coef = c(0.9, 0.3), rho = 0.9)

# Access properties
my_BiAR@coef

}
\references{
\insertRef{Elorrieta_2021}{iAR}
}
