Computes a marginal moment matching estimator @Section 6.2, @scealy2023scscorematchingad, which assumes \(\beta\) is a known vector with the same value in each element, and \(b_L = 0\). Only \(A_L\) is estimated.
ppi_mmmm(Y, ni, beta0, w = rep(1, nrow(Y)))A vector of estimates for \(A_L\) entries (diagonal and off diagonal).
Count data, each row is a multivariate observation.
The total for each sample (sum across rows)
\(\beta=\beta_0\) is the same for each component.
Weights for each observation. Useful for weighted estimation in Windham().
\(\beta=\beta_0\) is fixed and not estimated. \(b_L\) is fixed at zero. See @Section 6.2 and A.8 of @scealy2023scscorematchingad. The boundary weight function in the score matching discrepancy is the unthresholded product weight function $$h(z)^2 = \min\left(\prod_{j=1}^{p} z_j^2, a_c^2\right).$$