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logiBin

Binning Variables to Use in Logistic Regression

v0.3 · May 21, 2018 · GPL-2

Description

Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.

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Maintainer: ‘Sneha Tody <sn.tody1@gmail.com>’

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                    email = "sn.tody1@gmail.com")
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CRAN incoming feasibility

Maintainer: ‘Sneha Tody <sn.tody1@gmail.com>’

No Authors@R field in DESCRIPTION.
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  Authors@R: person(given = "Sneha",
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                    email = "sn.tody1@gmail.com")
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Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-debian-clang

CRAN incoming feasibility

Maintainer: ‘Sneha Tody <sn.tody1@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Sneha",
                    family = "Tody",
                    role = c("aut", "cre"),
                    email = "sn.tody1@gmail.com")
as necessary.
NOTE r-devel-linux-x86_64-debian-gcc

CRAN incoming feasibility

Maintainer: ‘Sneha Tody <sn.tody1@gmail.com>’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Sneha",
                    family = "Tody",
                    role = c("aut", "cre"),
                    email = "sn.tody1@gmail.com")
as necessary.

Dependency Network

Dependencies Reverse dependencies partykit doParallel data.table foreach iterators logiBin

Version History

new 0.3 Mar 9, 2026