The aggregate flows Y and their corresponding routing matrix A must include all aggregate source and destination flows.
1 2  tomogravity(Y, A, lambda, lower = 0, normalize = FALSE,
.progress = "none", control = list())

Y 
n x m matrix contain one vector of observed aggregate flows per row. This should include all observed aggegrate flows with none removed due to redundancy. 
A 
m x k routing matrix. This need not be of full row rank and must include all source and destination flows. 
lambda 
Regularization parameter for mutual information prior. Note that this is scaled by the squared total traffic in the objective function before scaling the mututal information prior. 
lower 
Componentwise lower bound for xt in LBFGSB optimization. 
normalize 
If TRUE, xt and yt are scaled by N. Typically used in conjunction with calcN to normalize traffic to proportions, easing the tuning of lambda. 
.progress 
name of the progress bar to use, see

control 
List of control information for optim. 
A list containing three elements:
resultList, a list containing the output from running
tomogravity.fit
on each timepoint
changeFromInit, a vector of length n containing the relative L_1 change between the initial (IPFP) pointtopoint flow estimates and the final tomogravity estimates
Xhat, a n x k matrix containing a vector of estimated pointtopoint flows (for each time point) per row
Other tomogravity: tomogravity.fit
1 2 3 
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.