c(
person("Alban", "Siffer", email="alban.siffer@irisa.fr", role= c("aut","cre")),
person("Amossys", email="contact@amossys.fr", role = c("cph", "fnd")))
The basic algorithm to perform the folding test of unimodality.
Given a dataset X (d dimensional, n samples), the test checks whether the
distribution of the data are rather unimodal or rather multimodal. This
package stems from the following research publication:
Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët.
"Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International
Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.
<doi:10.1145/3219819.3219994>.
Alban Siffer [aut, cre],
Amossys [cph, fnd]

Random Forests, Linear Trees, and Gradient Boosting for
Inference and Interpretability
c(
person("Sören", "Künzel", role = "aut"),
person("Theo", "Saarinen", role = c("aut","cre"), email = "theo_s@berkeley.edu"),
person("Simon", "Walter", role = "aut"),
person("Edward", "Liu", role = "aut"),
person("Allen", "Tang", role = "aut"),
person("Jasjeet", "Sekhon", role = "aut")
)
Provides fast implementations of Honest Random Forests,
Gradient Boosting, and Linear Random Forests, with an emphasis on inference
and interpretability. Additionally contains methods for variable
importance, out-of-bag prediction, regression monotonicity, and
several methods for missing data imputation. Soren R. Kunzel,
Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon (2019) <arXiv:1906.06463>.
Rcpp (>= 0.12.9), parallel, methods, visNetwork, glmnet (>=
4.1), grDevices, onehot, dplyr
'R_preprocessing.R' 'RcppExports.R' 'forestry.R'
'adaptive_forestry.R' 'backwards_compatible.R'
'compute_rf_lp.R' 'neighborhood_imputation.R' 'plottree.R'
Sören Künzel [aut],
Theo Saarinen [aut, cre],
Simon Walter [aut],
Edward Liu [aut],
Allen Tang [aut],
Jasjeet Sekhon [aut]

Rcpp (>= 0.12.9), parallel, methods, visNetwork, glmnet,
grDevices, onehot
'R_preprocessing.R' 'RcppExports.R' 'forestry.R'
'backwards_compatible.R' 'compute_rf_lp.R'
'neighborhood_imputation.R' 'plottree.R'