partition

At a glance

• Editor score
  3.0 / 10
• Pricing
  Free plan
• Best for
  Researchers doing interpretable dimensionality reduction
• Free plan
  Yes
• Paid from
  Free
• Deployment
  Desktop
• Facts checked
  23 Sep 2026

• Where it wins

  • Maps each original variable to exactly one reduced variable.
  • Configures variable selection, information loss and reduction methods.
  • Includes visualization and permutation-test functions.

• Where it doesn't

  • Focused on dimensionality reduction rather than general statistical analysis.
  • Requires working with R data frames or tibbles.
  • As an R package, it is not a hosted statistical-analysis application.

Our verdict on partition

partition is an open-source R package for researchers who want to reduce dimensionality while retaining interpretable links to their original variables. Its Direct-Measure-Reduce approach creates reduced variables while preserving a user-specified minimum information level. Each original variable maps to exactly one reduced variable, making the resulting structure easier to interpret than an opaque reduction alone. It is a focused tool for R workflows, not a general-purpose statistical-analysis application.

The package centers on agglomerative partitioning, with several choices for how variables are grouped and reduced. Users can configure the variable-selection strategy, information-loss metric and reduction function. Built-in partitioners include intraclass correlation (ICC), k-means, minimum-R², principal-component and standardized-mutual-information approaches. Visualization and permutation-test functions support examining partitions and their results. This combination suits analyses where the partitioning choices and information trade-offs need to remain explicit; researchers seeking a broader set of statistical procedures will need other software alongside it.

partition is distributed as open-source software through CRAN and GitHub, with desktop availability on Windows, macOS and Linux. It works with R data frames and tibbles, so it fits best where R is already part of the analysis workflow. Its open-source model avoids a commercial plan decision, while documentation and community are the listed support channels. Choose partition when interpretable variable partitioning is the main need and customizable reduction matters. Choose a broader statistical package or hosted application if you need analysis beyond this narrow dimensionality-reduction focus or do not want an R-based workflow.

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Alternatives to partition

• RA free, programmable environment for statistical analysis, modeling, visualization, and data import.9.4
• SStataA broad, scriptable desktop environment for professional and reproducible applied analysis.9.3
• IBM SPSS StatisticsA broad desktop statistics suite with testing, modeling, forecasting, reporting, and R/Python integration.9.2

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Featured on iTechGuides

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update