Learn R Programming

⚠️There's a newer version (1.0-7) of this package.Take me there.

tileHMM (version 1.0-4)

Hidden Markov Models for ChIP-on-Chip Analysis

Description

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

Copy Link

Version

Install

install.packages('tileHMM')

Monthly Downloads

1

Version

1.0-4

License

GPL (>= 2)

Maintainer

Peter Humburg

Last Published

May 13th, 2011

Functions in tileHMM (1.0-4)

Class "contDist"

State Names of Hidden Markov Model

Determine Length of Positive and Negative Regions

Sample Observations from Probability Distribution

Baum-Welch Algorithm

Create HMM from Parameter Values

Converting Information about Enriched Regions into GFF Format

Calculate 'Shrinkage t' Statistic

Generate Simulated Dataset

Calculate Posterior Probability for States of HMM

Post-Processing of "tileHMM" Results

Simulated ChIP-on-Chip Data

Generate Observation Sequence from HMM

Plotting of "contDist" Objects

tileHMM-package

Hidden Markov Models for ChIP-on-Chip Analysis

Efficient Estimation of HMM Parameters

Internal Functions

Create HMM from Initial Parameter Estimates Obtained from Data

Class "discDist"

Extract Probe Calls from GFF File

viterbiTraining

Estimate HMM Parameters Using Viterbi Training

region.position

Identify Enriched Regions

Class "contHMM"

initializeDist-methods

Generating Objects of Class 'dist'

Computation of Forward and Backward Variables

Accessing Objects of Class "contHMM"

initializeHMM-methods

Generate Objects of Class 'hmm'

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

Accessing and Converting Objects of Class "dist"

Calculate Most Likely State Sequence Using the Viterbi Algorithm