pandoc (to export an interactive summary table to a
file)

c(
person(given = "Laure", family = "Cougnaud", role = c("aut", "cre"),
email = "laure.cougnaud@openanalytics.eu"),
person(given = "Michela", family = "Pasetto", role = "aut",
email = "michela.pasetto@openanalytics.eu"),
person(given = "Margaux", family = "Faes",
role = "rev", comment = "tests"),
person(given = "Open Analytics", role = "cph")
)
clinUtils (>= 0.1.0), cowplot, flextable (>= 0.5.5), ggplot2,
ggrepel, magrittr, methods, officer, plyr, reshape2 (>= 1.4),
scales, stats, utils
htmltools, knitr, rmarkdown, pander, testthat, xml2, tools,
dplyr
Laure Cougnaud [aut, cre],
Michela Pasetto [aut],
Margaux Faes [rev] (tests),
Open Analytics [cph]

htmltools, knitr, rmarkdown, pander, testthat, xml2, tools,
dplyr, tibble

From a tree ensemble, extract, measure and prune rules; select a compact rule set; summarize rules into a learner; calculate frequent variable interactions.

For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.

RRF, arules, gbm, xtable, xgboost, data.table, methods
Houtao Deng, Xin Guan, Vadim Khotilovich

c(person("Houtao", "Deng", role = c("aut", "cre"),
email = "softwaredeng@gmail.com"),
person("Xin", "Guan", role = "aut"),
person("Vadim", "Khotilovich", role = "aut"))
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.  Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).
Houtao Deng [aut, cre],
Xin Guan [aut],
Vadim Khotilovich [aut]