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.

Install the latest version of this package by entering the following in R:
AuthorPeter Humburg [aut, cre]
Date of publication2015-07-03 00:25:58
MaintainerPeter Humburg <peter.humburg@gmail.com>
LicenseGPL (>= 2)

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


as.data.frame.discDist Man page
as.matrix.discDist Man page
as.vector.discDist Man page
backward Man page
_backward Man page
backward,hmm-method Man page
baumWelch Man page
.baumWelchEmission,contHMM,contDist,list-method Man page
.baumWelchEmission,contHMM,tDist,list-method Man page
baumWelch,hmm,list-method Man page
.baumWelchStep,contHMM,list-method Man page
_baumWelch_trans Man page
[<-.contDist Man page
[.contDist Man page
contDist-class Man page
[.contHMM Man page
contHMM-access Man page
contHMM-class Man page
[.discDist Man page
[[.discDist Man page
discDist-class Man page
dist-class Man page
forward Man page
_forward Man page
forward,hmm-method Man page
generate.data Man page
getHMM Man page
_get_obs_prob Man page
gff2index Man page
hmm-class Man page
hmm.setup Man page
initialize,contDist-method Man page
initialize,contHMM Man page
initialize,contHMM-method Man page
initialize,discDist-method Man page
initialize,tDist-method Man page
internals Man page
length,discDist-method Man page
length,hmm-method Man page
_log_sum Man page
logSum Man page
new,contDist-method Man page
new,contHMM-method Man page
new,discDist-method Man page
new,tDist-method Man page
plot Man page
plot.contDist Man page
plot.contHMM Man page
plot.tDist Man page
posterior Man page
reg2gff Man page
region.length Man page
region.position Man page
remove.short Man page
sampleObs Man page
sampleObs,discDist,numeric-method Man page
sampleObs,tDist,numeric-method Man page
sampleSeq Man page
sampleSeq,contHMM,numeric-method Man page
show,contDist-method Man page
show,hmm-method Man page
shrinkt.st Man page
simChIP Man page
states Man page
states,hmm-method Man page
[<-.tDist Man page
[.tDist Man page
tDist-class Man page
tileHMM Man page
tileHMM-package Man page
viterbi Man page
_viterbi Man page
viterbiEM Man page
viterbi,hmm-method Man page
viterbiTraining Man page
.viterbiTrainingEmission,contHMM,list,list-method Man page
viterbiTraining,hmm,list-method Man page

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

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

All documentation is copyright its authors; we didn't write any of that.