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.
methods, discretization (>= 1.0-1), glmnet (>= 3.0-0)

c(
person("Michael", "Hahsler", role=c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu",
comment = c(ORCID = "0000-0003-2716-1405")),
person("Ian", "Johnson", role=c("aut","cph")),
person("Tyler", "Giallanza", role=c("ctb"))
)
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.
Michael Hahsler [aut, cre, cph]
(<https://orcid.org/0000-0003-2716-1405>),
Ian Johnson [aut, cph],
Tyler Giallanza [ctb]

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>.

Mining NB-Frequent Itemsets and NB-Precise Rules
NBMiner is an implementation of the model-based mining
algorithm for mining NB-frequent itemsets presented in "Michael
Hahsler. A model-based frequency constraint for mining
associations from transaction data. Data Mining and Knowledge
Discovery, 13(2):137-166, September 2006." In addition an
extension for NB-precise rules is implemented.
All code is Copyright (C) Michael Hahsler

NBMiner is an implementation of the model-based mining algorithm
for mining NB-frequent itemsets presented in "Michael Hahsler. A
model-based frequency constraint for mining associations from
transaction data. Data Mining and Knowledge Discovery, 13(2):137-166,
September 2006." In addition an extension for NB-precise rules is
implemented.

c(person("Michael", "Hahsler", role = c("aut", "cre", "cph"),
email = "mhahsler@lyle.smu.edu"))
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>.