Iterative Proportional Fitting Algorithms for Nested Structures

The Iterative Proportional Fitting (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. The fitting algorithms include Iterative Proportional Updating <>, Hierarchical IPF , Entropy Optimization <>, and Generalized Raking . Additionally, a number of replication methods is also provided such as 'Truncate, replicate, sample' .


Reference manual

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0.5.2 by Amarin Siripanich, a month ago,

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Authors: Kirill Müller [aut, cph] (Creator of the package) , Kay W. Axhausen [ths] (Advisor of Kirill Müller) , Amarin Siripanich [aut, cre] (Contributed `ml_replicate()`) , Taha H. Rashidi [ths] (Advisor of Amarin Siripanich)

Documentation:   PDF Manual  

GPL (>= 3) license

Imports BB, dplyr, hms, kimisc, Matrix, plyr, tibble, forcats, rlang, utils, wrswoR, lifecycle

Depends on methods

Suggests covr, testthat, MASS, sampling, XML, waldo

See at CRAN