Reviewer response for version 1
The author presents a R based toolset for the analysis of ChIP-seq data in a GUI framework. 
The construction of R based ChIP-seq analysis pipelines affords the potential for the use of wide range of tools from R and Bioconductor libraries while offering a low dependency piece of software.
ChIPdig uses QuasR, a wrapper for Bowtie, for the alignment of ChIP-seq data from a BSGenome object. 
The Rbowtie2 and Rsubread packages are now both available on Windows, Mac and Linux systems and should be considered alongside Bowtie. 
I believe they would offer significant speed and memory usage improvements over QuasR. Although these do not accept BSGenome objects, ChIPdig could easily generate the FASTA from these packages for use with indexing steps of both packages.
Blacklisted regions should be considered in this tool as they have been shown to have strong effects on the QC, fragment length estimation and between sample normalisation. 
Inclusion of methods of blacklist filtering from known sources (such as Encode) or in software derived blacklists (using GreyListChIP) should be performed.
The output of BedGraph instead of BigWigs may cause some problems for users when working with larger genomes such as human or mouse. 
BigWigs may not be able to be exported on Windows systems but users of Mac or linux should have this option available to them to make this feature worthwhile.
Peak calling is performed with BayesPeak. 
It is unclear how this performs on the different types of epigenetic marks used in this study. 
Some more options for peak calling could be included here to allow finer control of the stitching of peaks into larger peaks. 
A simple bin based peak calling approach such as implemented in the CSAW user guide would be useful here. 
How the identification of replicated peaks is not clear in text and could be expanded.
The example differential enrichment analysis compare H3k4me3 and H3K36me3 signals directly. 
This is a strange example as most differential ChIP-seq analysis is performed within the same antibody. 
An example comparing the change in one histone mark over different conditions/treatment/tissue types would be a more useful and relevant comparison.
This differential enrichment example does highlight a potential pitfall with this approach where the majority of sites change. 
The user should be warned in these circumstances as conclusions are likely to be invalid. 
Alternative normalisations such as to total mapped reads in peaks or total mapped reads to genome could be provided as options (as in Diffbind).
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