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RJaCGH (version 1.1.1)

Reversible Jump MCMC for the analysis of CGH arrays.

Description

Bayesian analysis of CGH microarrays fitting Hidden Markov Chain models. The selection of the number of states is made via their posterior probability computed by Reversible Jump Markov Chain Monte Carlo Methods. Also returns probabilistic minimal common regions for gains/losses.

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Version

Install

install.packages('RJaCGH')

Monthly Downloads

102

Version

1.1.1

License

GPL version 2 or newer

Maintainer

Oscar Rueda

Last Published

February 15th, 2017

Functions in RJaCGH (1.1.1)

RJaCGH

Reversible Jump MCMC for the analysis of arrays of CGH
Q.NH

Transition Matrix for non-homogeneous Hidden Markov Model
genome.plot

Plot of the genome with probabilities of alteration.
snijders

Public CGH data of Snijders
pMCR

Probabilistic Minimal Common Regions for copy number alteration.
model.averaging

Method for model averaging for RJaCGH objects.
simulateRJaCGH

Simulate observations form a hidden Markov model with non-homogeneous transition probabilities.
collapseChain

Collapse several parallel chains ('RJaCGH' objects)
print.pMCR.RJaCGH

Method for printing probabilistic minimal common region.
plot.Q.NH

Plot transition probabilities
gelman.brooks.plot

gelman-brooks plot for 'RJaCGH' objects
states

'states' method for RJaCGH objects
chainsSelect

Select between several parallel chains ('RJaCGH' objects)
summary.RJaCGH

Summarizing RJaCGH models
normal.HMM.likelihood.NH.C

Likelihood for non-homogeneous hidden Markov model
trace.plot

Trace plot for 'RJaCGH' object
plot.RJaCGH

'plot' method for RJaCGH objects