Epigenomic R-based analysis with hidden Markov models

Documentation for package ‘epigraHMM’ version 1.15.0

addOffsets	Add offsets to epigraHMMDataSet
callPatterns	Extract posterior probabilities (or combinatorial patterns) associated with differential regions
callPeaks	Summarize peak calls and optionally create a BED 6+3 file in broadPeak format for visualization
cleanCounts	Remove effects from covariates of interest
controlEM	Control parameters for the EM algorithm from epigraHMM
epigraHMM	Perform peak calling of epigenomic data sets
epigraHMMDataSetFromBam	Create a epigraHMMDataSet from a set of BAM files
epigraHMMDataSetFromMatrix	Create a epigraHMMDataSet from matrices of counts
estimateTransitionProb	Estimate transition probability from a sequence of integers
expStep	E-step of HMM (forward-backward probability + posterior probability calculation)
helas3	ENCODE ChIP-seq broad data from Helas3 cell line
info	Get information about peak calling results
initializer	Initializer of epigraHMM
maxStepProb	M-step (maximization w.r.t. initial and transition probabilities)
normalizeCounts	Normalize counts
plotCounts	Create a plot with the results from epigraHMM
plotPatterns	Create a plot of differerential patterns posterior probabilities from epigraHMM
segmentGenome	Segmentation of a genome in non-overlapping windows
simulateMarkovChain	Simulates a Markov Chain of length 'n' given a matrix of transition probabilities P