Gibbs sampling of dynamic U and V with heavy-tailed (Student-t) innovations

Like rUV_dynamic_fc but the AR(1) innovations are Student-t via a scale-mixture of normals, a continuous heavy-tailed alternative to snap-shift.

Usage

rUV_dynamic_t_fc(
  U,
  V,
  ET,
  rho_uv,
  sigma_uv,
  s2,
  nu,
  lambda_u = NULL,
  lambda_v = NULL,
  shrink = TRUE,
  symmetric = FALSE
)

Arguments

U

3D array of current U positions (n x R x T)

V

3D array of current 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 (default TRUE)

symmetric

whether the network is symmetric (default FALSE)

Value

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

Author

Cassy Dorff, Shahryar Minhas, Tosin Salau