Title: Scores Features for Feature Selection

Package: FeaLect
Type: Package
Title: Scores features for Feature seLection
Version: 1.3
Date: 2010-10-28
Author: Habil Zare
Maintainer: Habil Zare <zare@cs.ubc.ca>
Depends: lars, rms
Description: For each feature, a score is computed that can be useful
for feature selection. Several random subsets are sampled from
the input data and for each random subset, various linear
models are fitted using lars method. A score is assigned to
each feature based on the tendency of LASSO in including that
feature in the models.Finally, the average score and the models
are returned as the output. The features with relatively low
scores are recommended to be ignored because they can lead to
overfitting of the model to the training data. Moreover, for
each random subset, the best set of features in terms of global
error is returned. They are useful for applying Bolasso, the
alternative feature selection method that recommends the
intersection of features subsets.
License: GPL (>= 2)
LazyLoad: yes
Repository: CRAN
Date/Publication: 2012-10-29 08:57:04
Packaged: 2012-10-29 08:57:04 UTC; ripley

Title: Implement Feature Hashing on Model Matrix
Authors@R: as.person(c( "Wush Wu <wush978@gmail.com> [aut, cre]", "Austin Appleby (for the included Murmurhash3 sources) [ctb]"))
Author: Wush Wu [aut, cre], Austin Appleby [ctb] (for the included Murmurhash3 sources)
Description: Feature hashing, also called as the hashing trick, is a method to
transform features to vector. Without looking the indices up in an
associative array, it applies a hash function to the features and uses their
This package implements the method of feature hashing proposed in
Weinberger et. al. (2009) with Murmurhash3 and provides a formula interface
in R. See the README.md for more information.

Title: Creates a Model Matrix via Feature Hashing With a Formula
Authors@R: as.person(c( "Wush Wu <wush978@gmail.com> [aut, cre]"))
transform features to vector. Without looking up the indices in an
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.md for more information.
Imports: Rcpp (>= 0.11), Matrix, digest(>= 0.6.8)