Provides algorithms for creating artworks in the 'ggplot2' language that incorporate some form of randomness.

Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.

Provides the infrastructure for representing, manipulating
    and analyzing transaction data and patterns (frequent itemsets and
    association rules).  Also provides C implementations of the
    association mining algorithms Apriori and Eclat.  Hahsler, Gruen and
    Hornik (2005) <doi:10.18637/jss.v014.i15>.

Provides the infrastructure for association rule-based classification including the algorithms 
  CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. 
  Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.

NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.

Add-on for arules to handle and mine frequent sequences.
    Provides interfaces to the C++ implementation of cSPADE by  
    Mohammed J. Zaki.

Extends package 'arules' with various visualization
    techniques for association rules and itemsets. The package also
    includes several interactive visualizations for rule exploration.
    Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.

Parse Autonomous Recording Unit (ARU) data and for sub-sampling recordings. 
    Extract Metadata from your recordings, select a subset of recordings for 
    interpretation, and prepare files for processing on the 
    'WildTrax' <https://wildtrax.ca/> platform. Read and process metadata 
    from recordings collected using the SongMeter and BAR-LT types of ARUs.