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.

Implements the 'Arvind' distribution and five novel stochastic
    regression models that replace the traditional Gaussian error assumption
    with 'Arvind'-distributed errors. The 'Arvind' distribution is a flexible
    single-parameter continuous distribution on the positive real line
    characterised by a polynomial numerator with Gaussian-type decay. The
    package provides complete distribution functions (darvind(), parvind(),
    qarvind(), rarvind()), maximum likelihood estimation via
    fit_arvind_mle(), and five model-fitting routines: Random Walk on
    Coefficients via fit_rw1(), Time-Varying Coefficient Linear Model via
    fit_tvlm(), Simulation-Extrapolation via fit_simex(), Mixed-Effects
    Regression via fit_mixed(), and Regime-Switching Hidden Markov Model via
    fit_hmm(). Additionally provides Monte Carlo forecasting with prediction
    intervals via forecast_arvind(), comprehensive goodness-of-fit
    diagnostics (21 metrics and 25 plots) via diagnostics_arvind() and
    plot_arvind(), k-fold and rolling-window cross-validation via
    cv_arvind(), and unified model comparison via summary_arvind().
    For more details see Pandey, Singh, Tyagi, and Tyagi (2024),
    "Modelling climate, COVID-19, and reliability data: A new continuous
    lifetime model under different methods of estimation",
    'Statistics and Applications', 22(2).