Fault Trees for Risk and Reliability Analysis
c(person("David", "Silkworth", email = "djsilk@openreliability.org", role = "aut"), person("Jacob", "Ormerod", email = "jake@openreliability.org", role = "cre"), person("OpenReliability.org", role = "cph"))
David Silkworth [aut],
Jacob Ormerod [cre],
OpenReliability.org [cph]

Full Length Circular RNA Sequence Extraction and Classification
Md. Tofazzal Hossain, Yin Peng, Shengzhong Feng and Yanjie Wei
Extract full length circular RNA sequences and classify circular RNA
using the output of circular RNA prediction tools, reference genome and the annotation file corresponding to the reference genome.
This package uses the output of circular RNA prediction tools such as 'CIRI', 'CIRCExplorer' and the output of other state-of-the-art circular RNA prediction tools.
Details about the circular RNA prediction procedure can be found in
'Yuan Gao, Jinfeng Wang and Fangqing Zhao' (2015) <doi:10.1186/s13059-014-0571-3>
and 'Zhang XO, Wang HB, Zhang Y, Lu X, Chen LL and Yang L' (2014) <doi:10.1016/j.cell.2014.09.001>.
R (>= 3.6.0), Biostrings, seqRFLP, stringi
2020-01-21 06:35:23 UTC; Md. Tofazzal Hossain

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.

Implement Feature Hashing on Model Matrix
as.person(c( "Wush Wu <wush978@gmail.com> [aut, cre]", "Austin Appleby (for the included Murmurhash3 sources) [ctb]"))
Wush Wu [aut, cre], Austin Appleby [ctb] (for the included Murmurhash3 sources)
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
hash values as indices directly.
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.