Title: Normalisation Tools for Inter-Condition Variability of ChIP-Seq
Maintainer: Andrew N Holding <andrew.holding@cruk.cam.ac.uk>
Description: Inter-sample condition variability is a key challenge of normalising ChIP-seq data. This implementation uses either spike-in or a second factor as a control for normalisation. Input can either be from 'DiffBind' or a matrix formatted for 'DESeq2'. The output is either a 'DiffBind' object or the default 'DESeq2' output. Either can then be processed as normal. Supporting manuscript Guertin, Markowetz and Holding (2017) <doi:10.1101/182261>.
Depends: DiffBind, Rsamtools, DESeq2, lattice, stats, utils, graphics
biocViews: Software, Technology, Sequencing, ChIPSeq

Depends: R (>= 2.10), DiffBind, Rsamtools, DESeq2, lattice, stats,

Title: Bayes Screening and Model Discrimination
Author: Ernesto Barrios based on Daniel Meyer's code.
Description: Bayes screening and model discrimination follow-up designs.

Packaged: Tue Mar  2 15:49:38 2004; ebarrios

Packaged: Fri Dec 10 19:16:52 2004; hornik

Packaged: Mon Feb 23 11:11:39 2009; hornik

Description: Bayes screening and model discrimination follow-up
Packaged: Thu Feb 26 08:39:30 2009; hornik

Packaged: Wed Feb  8 12:58:21 2006; hornik

Packaged: 2012-03-21 02:22:35 UTC; EBARRIOSZ

X-CRAN-Comment: Orphaned and corrected on 2018-02-01 as invalid use of

Title: A Deep Learning Package for Statistical Classification Analysis
Description: Statistical classification has been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades. It is a veritable technique which was originally designed for the classification, and hence, the EzDL package can provide sublime solutions to various challenging classification problems encountered in the clinical trials. The EzDL package is based on the back-propagation algorithm which performs a multi-layer feed-forward neural network. This package contains two functions: Buddle_Main() and Buddle_Predict(). Buddle_Main() builds a feed-forward neural network model and trains the model. Buddle_Predict() provokes the trained model which is the output of Buddle_Main(), classifies given data, and make a final prediction for the data.