Fit and simulate ARTFIMA. Theoretical autocovariance function and spectral density function for stationary ARTFIMA.

For instructions, check <https://github.com/Hzhang-ouce/ARTofR>. This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers.

The aligned rank transform for nonparametric
    factorial ANOVAs as described by Wobbrock,
    Findlater, Gergle, and Higgins (2011) <doi:10.1145/1978942.1978963>. 
    Also supports aligned rank transform contrasts as described
    by Elkin, Kay, Higgins, and Wobbrock (2021)
    <doi:10.1145/3472749.3474784>.

Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art.

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

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>.