ipf: Reweight a Seed Table to Marginal Controls
In pbsag/ipfr: Develop Expansion Weights for a Survey

Description Usage Arguments Value

Reweight a Seed Table to Marginal Controls

1 2	ipf(seed, targets, relative_gap = 0.01, absolute_gap = 1, max_iterations = 50, min_weight = 1e-04, verbose = FALSE)

`seed`	A `data frame` including a `weight` field and necessary colums for matching to marginal targets.
`targets`	A `named list` of data frames. Each name in the list defines a marginal dimension and must match a column from the seed table. The data frame associated with each name must start with an identical column named `cluster`. Each row in the target table defines a new cluster (these could be TAZs, tracts, districts, etc.), and every target table must have the same number of rows/clusters. The other column names define the marginal categories that targets are provided for.
`relative_gap`	target for convergence. Maximum percent change to allow any seed weight to move by while considering the process converged. By default, if no weights change by more than 1 The process is said to be converged if either `relative_gap` or `absolute_gap` parameters have been met.
`absolute_gap`	target for convergence. Maximum absolute change to allow any seed weight to move by while considering the process converged. By default, if no weights change by more than 10, the process has converged. The process is said to be converged if either `relative_gap` or `absolute_gap` parameters have been met.
`max_iterations`	maximimum number of iterations to perform, even if convergence is not reached.
`min_weight`	Minimum weight to allow in any cell to prevent zero weights. Set to .0001 by default. Should be arbitrarily small compared to your seed table weights.
`verbose`	Print details on the maximum expansion factor with each iteration? Default `FALSE`.