Autoregressive Process for Latent Dynamics — AR • EpiAwareR

Constructs an autoregressive (AR) latent process of order p for modeling time-varying log reproduction numbers or other epidemiological parameters.

AR(order = 1, damp_priors, init_priors, std_prior)

Arguments

order: Integer. The order of the autoregressive process (p).
damp_priors: List of distribution specifications for damping coefficients. Length must equal order.
init_priors: List of distribution specifications for initial states. Length must equal order.
std_prior: Distribution specification for innovation standard deviation.

Value

An S3 object of class c("epiaware_ar", "epiaware_latent", "epiaware_model") containing:

julia_ref: Reference to the Julia AR object
spec: List of model specifications

See also

epiaware_call for accessing other latent models

Examples

if (FALSE) { # \dontrun{
# AR(2) model with truncated normal priors (Mishra et al. 2020)
ar2 <- AR(
  order = 2,
  damp_priors = list(
    truncnorm(0.2, 0.2, 0, 1),
    truncnorm(0.1, 0.05, 0, 1)
  ),
  init_priors = list(norm(0, 0.2), norm(0, 0.2)),
  std_prior = halfnorm(0.1)
)
print(ar2)
} # }