gravity.fit

length-m numeric vector of observed aggregate
  flows at time t

list of source and destination flow
  indices corresponding to each point-to-point flow, as
  produced by <code><a rd-options="" href="/link/getSrcDstIndices?package=networkTomography&version=0.3" data-mini-rdoc="networkTomography::getSrcDstIndices">getSrcDstIndices</a></code>

srcDstInd


Gravity estimation for a single time point


multivariate

models

networkTomography implements the methods developed and evaluated in
Blocker and Airoldi (2011) and Airoldi and Blocker (2012). These include the
authors' own dynamic multilevel model with calibration based upon a Gaussian
state-space model in addition to implementations of the methods of Tebaldi &
West (1998; Poisson-Gamma model with MCMC sampling), Zhang et al. (2002;
tomogravity), Cao et al. (2000; Gaussian model with mean-variance relation),
and Vardi (1996; method of moments). Data from the 1router network of Cao et
al. (2000), the Abilene network of Fang et al. (2007), and the CMU network
of Blocker and Airoldi (2011) are included for testing and reproducibility.

gravity.fit: Gravity estimation for a single time point

Description

Usage

Arguments

Value

See Also

Examples