Learn R Programming

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

tileHMM (version 1.0-3)

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

1.0-3

License

GPL (>= 2)

Maintainer

Peter Humburg

Last Published

September 29th, 2009

Functions in tileHMM (1.0-3)

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

Class "discDist"

Plotting of "contDist" Objects

initializeHMM-methods

Generate Objects of Class 'hmm'

region.position

Identify Enriched Regions

Create HMM from Initial Parameter Estimates Obtained from Data

Generate Observation Sequence from HMM

tileHMM-package

Hidden Markov Models for ChIP-on-Chip Analysis

Internal Functions

Baum-Welch Algorithm

Calculate Most Likely State Sequence Using the Viterbi Algorithm

Efficient Estimation of HMM Parameters

Determine Length of Positive and Negative Regions

Generate Simulated Dataset

State Names of Hidden Markov Model

viterbiTraining

Estimate HMM Parameters Using Viterbi Training

Accessing Objects of Class "contHMM"

Accessing and Converting Objects of Class "dist"

Class "contDist"

Class "contHMM"

Computation of Forward and Backward Variables

Post-Processing of "tileHMM" Results

Simulated ChIP-on-Chip Data

Calculate 'Shrinkage t' Statistic

Create HMM from Parameter Values

Extract Probe Calls from GFF File

Converting Information about Enriched Regions into GFF Format

initializeDist-methods

Generating Objects of Class 'dist'

Calculate Posterior Probability for States of HMM

Sample Observations from Probability Distribution