partition

Spec sheet

Purpose

partition is an R framework for agglomerative data reduction using a Direct-Measure-Reduce approach.Read 12 Oct 2026 · uscbiostats.github.io

Deployment

desktopRead 23 Sep 2026 · uscbiostats.github.io

Free plan

YesRead 23 Sep 2026 · uscbiostats.github.io

Support

The project directs users to GitHub to report bugs and provides contributor guidelines and a code of conduct.Read 12 Oct 2026 · uscbiostats.github.io

License

partition is released under the MIT license.Read 12 Oct 2026 · uscbiostats.github.io

Authors

The site lists Joshua Millstein, Malcolm Barrett, and Katelyn Queen as developers, with Malcolm Barrett identified as maintainer.Read 12 Oct 2026 · uscbiostats.github.io

Built-in methods

Built-in partitioners include ICC, K-means, minimum R-squared, principal component 1, and standardized mutual information options.Read 12 Oct 2026 · uscbiostats.github.io

Citation

The site cites a 2020 Bioinformatics paper titled “Partition: A Surjective Mapping Approach for Dimensionality Reduction.”Read 11 Oct 2026 · uscbiostats.github.io

Custom partitioners

Users can create custom partitioners from functions that select variables, measure information loss, and reduce data.Read 12 Oct 2026 · uscbiostats.github.io

Customizable

Users can customize which variables are selected, how information loss is measured, and how data is reduced.Read 12 Oct 2026 · uscbiostats.github.io

Customization

Users can customize variable selection, how information loss is measured, and how data is reduced.Read 11 Oct 2026 · uscbiostats.github.io

Data import formats

R data frames/tibblesRead 23 Sep 2026 · uscbiostats.github.io

Developers

The site lists Joshua Millstein, Malcolm Barrett, and Katelyn Queen as developers and identifies Malcolm Barrett as maintainer.Read 11 Oct 2026 · uscbiostats.github.io

Information threshold

The partition() function reduces a data frame to as few variables as possible while meeting a user-specified information threshold.Read 12 Oct 2026 · uscbiostats.github.io

Installation

The package can be installed from CRAN or from the USCbiostats GitHub repository.Read 12 Oct 2026 · uscbiostats.github.io

Interpretability

Each original variable maps to exactly one variable in the reduced data set.Read 12 Oct 2026 · uscbiostats.github.io

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.Read 12 Oct 2026 · uscbiostats.github.io

Methods

Built-in partitioners include methods based on intraclass correlation, K-means, minimum R-squared, principal component 1, and standardized mutual information.Read 11 Oct 2026 · uscbiostats.github.io

Performance

The documentation reports benchmarking on a data frame with one million rows using a 2017 MacBook Pro with 16 GB RAM.Read 12 Oct 2026 · uscbiostats.github.io

Permutation testing

test_permutation() compares observed partition statistics with summaries from permuted data sets and defaults to 100 permutations.Read 12 Oct 2026 · uscbiostats.github.io

Scaling

The site says the default ICC method scales more linearly than K-means methods as the number of columns exceeds the number of rows, while K-means is often faster at lower dimensions.Read 11 Oct 2026 · uscbiostats.github.io

Scripting support

YesRead 23 Sep 2026 · uscbiostats.github.io

Statistical tests

YesRead 23 Sep 2026 · uscbiostats.github.io

Use case

The package is particularly useful for highly correlated data, including genomic data with redundant features.Read 12 Oct 2026 · uscbiostats.github.io

Visualization

Partition visualizations return ggplot objects that can be extended with ggplot2.Read 12 Oct 2026 · uscbiostats.github.io

Where Specifiction places it

• Statistical Analysis Software#91 of 1067.1