Implementation of algorithms that extend IPF to nested structures.
The IPF algorithm operates on count data. This package offers implementations for several algorithms that extend this to nested structures: “parent” and “child” items for both of which constraints can be provided.
grake
: A reimplementation of generalized raking (Deville and Särndal, 1992; Deville, Särndal and Sautory, 1993)wrswoR
: An implementation of fast weighted random sampling without replacement (Efraimidis and Spirakis, 2006)
mangow
: Embed the Gower distance metric in L1
RANN.L1
: k-nearest neighbors using the L1 metric
devtools::install_github("krlmlr/MultiLevelIPF")
compute_margins
(margin_to_df)fitting_problem
(format.fitting_problem, is.fitting_problem, print.fitting_problem, special_field_names)flatten_ml_fit_problem
(as.flat_ml_fit_problem)ml_fit
(ml_fit_dss, ml_fit_entropy_o, ml_fit_hipf, ml_fit_ipu)MultiLevelIPF-package
(MultiLevelIPF)toy_example