Sample dynamic additive effects with AR(1) evolution

Gibbs update of the per-period additive effects under the AR(1) state model with stationary initial condition. For each actor and period the full conditional combines the AR(1) bridge prior (stationary init at t = 1) with the dyadic residual likelihood: resid_ij = a_i + b_j + e_ij, e_ij ~ N(0, s2). The reciprocal effect (b_j for the a-step, the freshly updated a_i for the b-step) is subtracted from each residual, missing residuals are skipped, and for symmetric networks each dyad contributes exactly once (resid(i,j) = a_i + a_j + e_ij).

Usage

sample_dynamic_ab_cpp(
  a_current,
  b_current,
  Z_array,
  EZ_array,
  rho_ab,
  sigma_ab,
  s2,
  symmetric
)

Arguments

a_current

Current 2D array of row effects (n x T)

b_current

Current 2D array of column effects (n x T)

Z_array

3D array of latent positions (n x n x T)

EZ_array

3D array of expected values without additive effects (n x n x T)

rho_ab

AR(1) parameter for additive effects

sigma_ab

Innovation standard deviation

s2

Dyadic residual variance

symmetric

Whether the network is symmetric

Value

List with updated a and b arrays