tileHMM: Hidden Markov Models for ChIP-on-Chip Analysis

Methods and classes to build HMMs that are suitable for the analysis of ChIP-chip data. The provided parameter estimation methods include the Baum-Welch algorithm and Viterbi training as well as a combination of both.

AuthorPeter Humburg [aut, cre]
Date of publication2015-07-03 00:25:58
MaintainerPeter Humburg <peter.humburg@gmail.com>
LicenseGPL (>= 2)
Version1.0-7

View on CRAN

Man pages

baumWelch: Baum-Welch Algorithm

contDist-class: Class "contDist"

contHMM-access: Accessing Objects of Class "contHMM"

contHMM-class: Class "contHMM"

discDist-class: Class "discDist"

dist-access: Accessing and Converting Objects of Class "dist"

dist-class: Class "dist"

forward: Computation of Forward and Backward Variables

generate.data: Generate Simulated Dataset

getHMM: Create HMM from Parameter Values

gff2index: Extract Probe Calls from GFF File

hmm-class: Class "hmm"

hmm.setup: Create HMM from Initial Parameter Estimates Obtained from...

initializeDist-methods: Generating Objects of Class 'dist'

initializeHMM-methods: Generate Objects of Class 'hmm'

internals: Internal Functions

logSum: Calculate log(x + y) from log(x) and log(y)

plot: Plotting of "contDist" Objects

posterior: Calculate Posterior Probability for States of HMM

reg2gff: Converting Information about Enriched Regions into GFF Format

region.length: Determine Length of Positive and Negative Regions

region.position: Identify Enriched Regions

remove.short: Post-Processing of "tileHMM" Results

sampleObs: Sample Observations from Probability Distribution

sampleSeq: Generate Observation Sequence from HMM

shrinkt.st: Calculate 'Shrinkage t' Statistic

simChIP: Simulated ChIP-on-Chip Data

states: State Names of Hidden Markov Model

tDist-class: Class "tDist"

tileHMM-package: Hidden Markov Models for ChIP-on-Chip Analysis

viterbi: Calculate Most Likely State Sequence Using the Viterbi...

viterbiEM: Efficient Estimation of HMM Parameters

viterbiTraining: Estimate HMM Parameters Using Viterbi Training

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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