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mlfit (version 0.5.3)

Iterative Proportional Fitting Algorithms for Nested Structures

Description

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' .

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Install

install.packages('mlfit')

Monthly Downloads

223

Version

0.5.3

License

GPL (>= 3)

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Maintainer

Amarin Siripanich

Last Published

October 8th, 2021

Functions in mlfit (0.5.3)

flatten_ml_fit_problem

Return a flattened representation of a multi-level fitting problem instance
ml_fit

Estimate weights for a fitting problem
ml_replicate

Replicate records in a reference sample based on its fitted weights
toy_example

Access to toy examples bundled in this package
fitting_problem

Create a fitting problem
ml_problem

Create an instance of a fitting problem
mlfit-package

mlfit: Iterative Proportional Fitting Algorithms for Nested Structures
gginv

Generalized Inverse of a Matrix using a custom tolerance or SVD implementation
compute_margins

Compute margins for a weighting of a multi-level fitting problem
dss

Calibrate sample weights