Package: nuggets 2.2.1

nuggets: Extensible Framework for Data Pattern Exploration

A framework for systematic exploration of association rules (Agrawal et al., 1994, <https://www.vldb.org/conf/1994/P487.PDF>), contrast patterns (Chen, 2022, <doi:10.48550/arXiv.2209.13556>), emerging patterns (Dong et al., 1999, <doi:10.1145/312129.312191>), subgroup discovery (Atzmueller, 2015, <doi:10.1002/widm.1144>), and conditional correlations (Hájek, 1978, <doi:10.1007/978-3-642-66943-9>). User-defined functions may also be supplied to guide custom pattern searches. Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions expressed as elementary conjunctions, evaluates them on a dataset, and inspects the induced sub-data for statistical, logical, or structural properties such as associations, correlations, or contrasts. Includes methods for visualization of logical structures and supports interactive exploration through integrated Shiny applications.

Authors:Michal Burda [aut, cre]

nuggets_2.2.1.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
nuggets/json (API)

# Install 'nuggets' in R:
install.packages('nuggets', repos = c('https://beerda.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/beerda/nuggets/issues

Pkgdown/docs site:https://beerda.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

association-rule-miningcontrast-pattern-miningdata-miningfuzzyknowledge-discoverypattern-recognitioncpp

6.50 score 7 stars 14 scripts 570 downloads 34 exports 54 dependencies

Last updated from:8a2ad33af8. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK287
linux-devel-x86_64OK254
source / vignettesOK359
linux-release-arm64OK262
linux-release-x86_64OK238
macos-release-arm64OK200
macos-release-x86_64OK408
macos-oldrel-arm64OK155
macos-oldrel-x86_64OK653
windows-develOK250
windows-releaseOK308
windows-oldrelOK320
wasm-releaseFAIL133

Exports:add_interestassociation_matrixbound_rangecluster_associationsdigdig_ancestorsdig_associationsdig_baseline_contrastsdig_complement_contrastsdig_correlationsdig_griddig_paired_baseline_contrastsdig_tautologiesexplorefireformat_conditiongeom_diamondis_almost_constantis_conditionis_degreeis_logicalishis_nuggetis_subsetnuggetparse_conditionpartitionplot_contingencyremove_almost_constantremove_ill_conditionsshorten_conditionvaluesvar_gridvar_nameswhich_antichain

Dependencies:briocallrclassclassIntclicpp11crayondescdiffobjdplyre1071evaluatefarverfastmatchfsgenericsggplot2gluegtableisobandjsonliteKernSmoothlabelinglifecyclemagrittrMASSotelpillarpkgbuildpkgconfigpkgloadpraiseprocessxproxypspurrrR6RColorBrewerRcpprlangrprojrootS7scalesstringistringrtestthattibbletidyrtidyselectutf8vctrsviridisLitewaldowithr

nuggets: Get Started
Introduction | Data Preparation | Crisp (Boolean) Predicates Example | Fuzzy Predicates Example | Advanced Data Preparation Capabilities | Pre-defined Patterns | Search for Association Rules | Conditional Correlations | Contrast Patterns | Baseline Contrasts | Complement Contrasts | Paired Baseline Contrasts | Post-processing and Visualization | Visualizing Association Rules with Diamond Plots | Interactive Exploration | Advanced Use | Custom Patterns with dig() | Grid-Based Patterns with dig_grid() | Summary | Next Steps
Rendered fromnuggets.Rmdusingknitr::rmarkdown

Last update: 2026-02-04
Started: 2024-11-16

Data Preparation
Introduction | Data Preparation with partition() | Preparation of Crisp (Boolean) Predicates | Data-Driven Breakpoint Selection with .style | Preparation of Triangular and Raised-Cosine Fuzzy Predicates | Preparation of Trapezoidal Fuzzy Predicates | Identifying and Removing Uninformative Columns | Testing for Almost Constant Columns | Removing Almost Constant Columns | Finding Tautologies in Data | What are Tautologies? | Using dig_tautologies() | Parameters | Using Tautologies to Filter Searches | Summary
Rendered fromdata-preparation.Rmdusingknitr::rmarkdown

Last update: 2025-11-05
Started: 2025-11-05

Readme and manuals

Help Manual

Help pageTopics
Add additional interest measures for association rulesadd_interest add_interest.associations
Create an association matrix from a nugget of flavour 'associations'.association_matrix
Bound a range of numeric valuesbound_range
Cluster association rulescluster_associations
Search for patterns of a custom typedig
Dig ancestors of an association ruledig_ancestors dig_ancestors.associations
Search for association rulesdig_associations
Search for conditions that yield in statistically significant one-sample test in selected variables.dig_baseline_contrasts
Search for conditions that provide significant differences in selected variables to the rest of the data tabledig_complement_contrasts
Search for conditional correlationsdig_correlations
Search for grid-based rulesdig_grid
Search for conditions that provide significant differences between paired variablesdig_paired_baseline_contrasts
Find tautologies or "almost tautologies" in a datasetdig_tautologies
Show interactive application to explore association rulesexplore.associations
Show interactive application to explore baseline contrastsexplore.baseline_contrasts
Show interactive application to explore complement contrastsexplore.complement_contrasts
Show interactive application to explore conditional correlationsexplore.correlations
Show interactive application to explore paired baseline contrastsexplore.paired_baseline_contrasts
Obtain truth-degrees of conditionsfire
Format a vector of predicates into a condition stringformat_condition
Geom for drawing diamond plots of lattice structuresgeom_diamond
Test whether a vector is almost constantis_almost_constant
Check whether a list of character vectors contains valid conditionsis_condition
Test whether an object contains numeric values from the interval [0,1]is_degree
Check if an object is logical or numeric with only 0s and 1sis_logicalish
Test whether an object is a nuggetis_nugget
Determine whether one vector is a subset of anotheris_subset
Create a nugget object of a given flavournugget
Convert condition strings into lists of predicate vectorsparse_condition
Convert data-frame columns into Boolean or fuzzy predicatespartition
Plot a mosaic plot for a contingency tableplot_contingency plot_contingency.data.frame plot_contingency.default
Remove almost constant columns from a data frameremove_almost_constant
Remove invalid conditions from a listremove_ill_conditions
Shorten predicates within conditionsshorten_condition
Extract values from predicate namesvalues
Create a tibble of combinations of selected column namesvar_grid
Extract variable names from predicate namesvar_names
Return indices of first elements of the list, which are incomparable with preceding elements.which_antichain