Title: Creates a Model Matrix via Feature Hashing with a Formula
person("Wush", "Wu", email = "wush978@gmail.com", role = c("aut", "cre")),
person("Michael", "Benesty", email = "michael@benesty.fr", role = c("aut", "ctb")))
Description: Feature hashing, also called as the hashing trick, is a method to transform
features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix.
Without looking up the indices in an associative array,
it applies a hash function to the features and uses their hash values as indices directly.
The method of feature hashing in this package was proposed in Weinberger et al. (2009).
The hashing algorithm is the murmurhash3 from the digest package.
Please see the README in https://github.com/wush978/FeatureHashing for more information.
Imports: Rcpp (>= 0.11), Matrix, digest(>= 0.6.8), magrittr (>= 1.5)
Suggests: RUnit, glmnet, knitr, xgboost, rmarkdown

The method of feature hashing in this package was proposed in Weinberger et al. (2009) <arXiv:0902.2206>.
The hashing algorithm is the murmurhash3 from the 'digest' package.
Please see the README in <https://github.com/wush978/FeatureHashing> for more information.

Suggests: RUnit, glmnet, knitr, xgboost, rmarkdown, pROC

Title: Feature Importance for Partitional Clustering
Description: Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values.
Suggests: flexclust, clustMixType, knitr, rmarkdown, testthat, attempt,

Description: Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: <https://christophm.github.io/interpretable-ml-book/feature-importance.html>.