Update dynamic latent positions with snap-shift model selection

Like rUV_dynamic_fc_cpp but, for t > 0, chooses per actor between an AR(1) drift prior and a diffuse N(0, kappa^2 I) snap prior via a Gaussian log-marginal-likelihood model selection, drawing a Bernoulli snap indicator delta and sampling the latent position from the selected posterior.

Usage

rUV_dynamic_snap_fc_cpp(
  U_current,
  V_current,
  ET,
  rho_uv,
  sigma_uv,
  s2,
  kappa,
  pi_snap,
  delta_u_current,
  delta_v_current,
  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 for the drift prior

sigma_uv

Innovation standard deviation for the drift prior

s2

Dyadic variance

kappa

Diffuse snap-prior standard deviation (kappa^2 >> sigma_uv^2)

pi_snap

Prior snap probability

delta_u_current

Current sender-side snap indicators from the previous sweep

delta_v_current

Current receiver-side snap indicators from the previous sweep

shrink

Whether to apply shrinkage

symmetric

Whether network is symmetric

Value

List with updated U, V arrays and delta_u, delta_v snap indicators