mipfp: An R package for multidimensional array fitting and simulating multivariate bernoulli distributions

Johan Barthélemy, Thomas Suesse

Research output: Contribution to journalArticle

Abstract

This paper explains the mipfp package for R with the core functionality of updating an d-dimensional array with respect to given target marginal distributions, which in turn can be multi-dimensional. The implemented methods include the iterative proportional fitting procedure (IPFP), the maximum likelihood method, the minimum chi-square and least squares procedures. The package also provides an application of the IPFP to simulate data from a multivariate Bernoulli distribution. The functionalities of the package are illustrated through two practical examples: the update of a 3-dimensional contingency table to match the targets for a synthetic population and the estimation and simulation of the joint distribution of the binary attribute impaired pulmonary function as used by Qaqish, Zink, and Preisser (2012).

Original languageEnglish
JournalJournal of Statistical Software
Volume86
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

Fingerprint

Multidimensional Arrays
Bernoulli
Maximum likelihood
Directly proportional
Target
Chi-square
Maximum Likelihood Method
Contingency Table
Marginal Distribution
Joint Distribution
Updating
Least Squares
Update
Attribute
Binary
Multivariate distribution
Simulation
Functionality

Keywords

  • Iterative proportional fitting procedure
  • Maximum likelihood
  • Minimum chi-square
  • Minimum least squares
  • Multivariate bernoulli distributions
  • R

Cite this

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