randomForest, rgl, RColorBrewer, Rtsne, som, stats, umap, utils

randomForest, RColorBrewer, Rtsne, som, stats, umap, utils

Imports: graphics, grDevices, lle, locfit, matrixStats, pheatmap,
princurve, randomForest, RColorBrewer, Rtsne, som, stats, umap,

Imports: graphics, grDevices, locfit, matrixStats, pheatmap, princurve,

Title: Fault Trees for Risk and Reliability Analysis
Authors@R: c(person("David", "Silkworth", email = "djsilk@openreliability.org", role = "aut"), person("Jacob", "Ormerod", email = "jake@openreliability.org", role = "cre"), person("OpenReliability.org", role = "cph"))
Copyright: copyright (c) OpenReliability.org 2015-2020

Package: FcircSEC
Title: Full Length Circular RNA Sequence Extraction and Classification
Version: 1.0.0
Author: Md. Tofazzal Hossain, Yin Peng, Shengzhong Feng and Yanjie Wei
Maintainer: Md. Tofazzal Hossain <tofazzal.stat@gmail.com>
Description: 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>.
URL: https://github.com/tofazzal4720/FcircSEC
BugReports: https://github.com/tofazzal4720/FcircSEC/issues
Depends: R (>= 3.6.0), Biostrings, seqRFLP, stringi
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.0
NeedsCompilation: no
Packaged: 2020-01-21 06:35:23 UTC; Md. Tofazzal Hossain
Repository: CRAN
Date/Publication: 2020-01-31 15:50:02 UTC

Package: FeaLect
Type: Package
Title: Scores features for Feature seLection
Version: 1.1
Date: 2010-10-28
Author: Habil Zare
Maintainer: Habil Zare <zare@cs.ubc.ca>
Depends: lars, Design
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
Packaged: 2010-11-02 01:50:17 UTC; habil
Repository: CRAN
Date/Publication: 2010-11-03 07:32:50