Usage
grad_smoothed(logtheta, c, M, rdiag, eta0, sigma0, V, eps.lambda, eps.phi)
Arguments
logtheta
numeric vector (length k+1) of
log(lambda) (1:k) and log(phi) (last entry)
c
power parameter in model of Cao et al. (2000)
M
matrix (n x k) of conditional expectations for
OD flows, one time per row
rdiag
numeric vector (length k) containing
diagonal of conditional covariance matrix R
eta0
numeric vector (length k+1) containing value
for log(c(lambda, phi)) from previous time (or initial
value)
sigma0
covariance matrix (k+1 x k+1) of
log(c(lambda, phi)) from previous time (or initial
value)
V
evolution covariance matrix (k+1 x k+1) for
log(c(lambda, phi)) (random walk)
eps.lambda
numeric small positive value to add to
lambda for numerical stability; typically 0
eps.phi
numeric small positive value to add to phi
for numerical stability; typically 0