# Epigenomic R-based analysis with hidden Markov models

## Documentation for package ‘epigraHMM’ version 1.4.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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