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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.

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Installation

devtools::install_github("krlmlr/MultiLevelIPF")

Help topics

  • compute_margins(margin_to_df)
    Compute margins for a weighting of a multi-level fitting problem
  • fitting_problem(format.fitting_problem, is.fitting_problem, print.fitting_problem, special_field_names)
    Create an instance of a fitting problem
  • flatten_ml_fit_problem(as.flat_ml_fit_problem)
    Return a flattened representation of a multi-level fitting problem instance
  • ml_fit(ml_fit_dss, ml_fit_entropy_o, ml_fit_hipf, ml_fit_ipu)
    Estimate weights for a fitting problem
  • MultiLevelIPF-package(MultiLevelIPF)
    Implementation of algorithms that extend IPF to nested structures
  • toy_example
    Access to toy examples bundled in this package

Dependencies

  • Depends: methods
  • Imports: plyr, dplyr, BB, Matrix, grake, hms, kimisc
  • Suggests: testthat, XML

Authors