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.
|Author||Peter Humburg [aut, cre]|
|Date of publication||2015-07-03 00:25:58|
|Maintainer||Peter Humburg <email@example.com>|
|License||GPL (>= 2)|
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