Update dynamic latent positions with heavy-tailed (Student-t) AR(1) innovations

Like rUV_dynamic_fc_cpp but each AR(1) innovation is Student-t rather than Gaussian, via a scale-mixture: the innovation for u[t, i] has variance sigma^2 / lambda[t, i] with lambda[t, i] distributed as Gamma(nu/2, nu/2). Provides a continuous heavy-tailed alternative to the discrete snap-shift model.

Usage

rUV_dynamic_t_fc_cpp(
  U_current,
  V_current,
  ET,
  rho_uv,
  sigma_uv,
  s2,
  nu,
  lambda_u,
  lambda_v,
  shrink,
  symmetric
)

Arguments

U_current: Current 3D array of U positions (n x R x T)
V_current: Current 3D array of V positions (n x R x T)
ET: 3D array of residuals (n x n x T)
rho_uv: AR(1) autoregressive parameter
sigma_uv: Innovation scale
s2: Dyadic variance
nu: Student-t degrees of freedom
lambda_u: Current local scales for U (n x T)
lambda_v: Current local scales for V (n x T)
shrink: Whether to apply shrinkage
symmetric: Whether network is symmetric

Value

List with updated U, V arrays and lambda_u, lambda_v local scales