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.
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
)List with updated U, V arrays and delta_u, delta_v snap indicators
Current 3D array of U positions (n x R x T)
Current 3D array of V positions (n x R x T)
3D array of residuals (n x n x T)
AR(1) autoregressive parameter for the drift prior
Innovation standard deviation for the drift prior
Dyadic variance
Diffuse snap-prior standard deviation (kappa^2 >> sigma_uv^2)
Prior snap probability
Current sender-side snap indicators from the previous sweep
Current receiver-side snap indicators from the previous sweep
Whether to apply shrinkage
Whether network is symmetric