- Home
- Statistical Analysis Software
- partition
On the recordRead 12 Oct 202625 facts
partition
On the record
- Authors
- The site lists Joshua Millstein, Malcolm Barrett, and Katelyn Queen as developers, with Malcolm Barrett identified as maintainer.uscbiostats.github.io · read 12 Oct 2026
- Built-in methods
- Built-in partitioners include ICC, K-means, minimum R-squared, principal component 1, and standardized mutual information options.uscbiostats.github.io · read 12 Oct 2026
- Citation
- The site cites a 2020 Bioinformatics paper titled “Partition: A Surjective Mapping Approach for Dimensionality Reduction.”uscbiostats.github.io · read 11 Oct 2026
- Customizable
- Users can customize which variables are selected, how information loss is measured, and how data is reduced.uscbiostats.github.io · read 12 Oct 2026
- Customization
- Users can customize variable selection, how information loss is measured, and how data is reduced.uscbiostats.github.io · read 11 Oct 2026
- Custom partitioners
- Users can create custom partitioners from functions that select variables, measure information loss, and reduce data.uscbiostats.github.io · read 12 Oct 2026
- Data import formats
- R data frames/tibblesuscbiostats.github.io · read 23 Sep 2026
- Deployment
- desktopuscbiostats.github.io · read 23 Sep 2026
- Developers
- The site lists Joshua Millstein, Malcolm Barrett, and Katelyn Queen as developers and identifies Malcolm Barrett as maintainer.uscbiostats.github.io · read 11 Oct 2026
- Information threshold
- The partition() function reduces a data frame to as few variables as possible while meeting a user-specified information threshold.uscbiostats.github.io · read 12 Oct 2026
- Installation
- The package can be installed from CRAN or from the USCbiostats GitHub repository.uscbiostats.github.io · read 12 Oct 2026
- Interpretability
- Each original variable maps to exactly one variable in the reduced data set.uscbiostats.github.io · read 12 Oct 2026
- Large feature sets
- super_partition() handles data with many features by first dividing it into smaller clusters, with a default maximum cluster size of 4000.uscbiostats.github.io · read 12 Oct 2026
- License
- partition is released under the MIT license.uscbiostats.github.io · read 12 Oct 2026
- Methods
- Built-in partitioners include methods based on intraclass correlation, K-means, minimum R-squared, principal component 1, and standardized mutual information.uscbiostats.github.io · read 11 Oct 2026
- Performance
- The documentation reports benchmarking on a data frame with one million rows using a 2017 MacBook Pro with 16 GB RAM.uscbiostats.github.io · read 12 Oct 2026
- Permutation testing
- test_permutation() compares observed partition statistics with summaries from permuted data sets and defaults to 100 permutations.uscbiostats.github.io · read 12 Oct 2026
- Purpose
- partition is an R framework for agglomerative data reduction using a Direct-Measure-Reduce approach.uscbiostats.github.io · read 12 Oct 2026
