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AnaCoDa
View on CRAN: Click
here
Download and install AnaCoDa package within the R console
Install from CRAN:
install.packages("AnaCoDa")
Install from Github:
library("remotes")
install_github("cran/AnaCoDa")
Install by package version:
library("remotes")
install_version("AnaCoDa", "0.1.4.4")
Attach the package and use:
library("AnaCoDa")
Maintained by
Cedric Landerer
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-01-11
Latest Update: 2020-09-15
Description:
Is a collection of models to analyze genome scale codon
data using a Bayesian framework. Provides visualization
routines and checkpointing for model fittings. Currently
published models to analyze gene data for selection on codon
usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
et al. (2015) ), and ROC with phi
(Wallace & Drummond (2013) ). In
addition 'AnaCoDa' contains three currently unpublished models.
The FONSE (First order approximation On NonSense Error) model
analyzes gene data for selection on codon usage against of
nonsense error rates. The PA (PAusing time) and PANSE (PAusing
time + NonSense Error) models use ribosome footprinting data to
analyze estimate ribosome pausing times with and without
nonsense error rate from ribosome footprinting data.
How to cite:
Cedric Landerer (2018). AnaCoDa: Analysis of Codon Data under Stationarity using a Bayesian Framework. R package version 0.1.4.4, https://cran.r-project.org/web/packages/AnaCoDa. Accessed 21 Nov. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for AnaCoDa
Functions, R codes and Examples using
the AnaCoDa R package
Some associated functions: AAToCodon . acfCSP . acfMCMC . addObservedSynthesisRateSet . aminoAcids . calculateMarginalLogLikelihood . calculateSCUO . codonToAA . codons . convergence.test . findOptimalCodon . fixDEta . fixDM . fixSphi . geomMean . getAdaptiveWidth . getCAI . getCAIweights . getCSPEstimates . getCodonCounts . getCodonCountsForAA . getCodonSpecificPosteriorMeanForCodon . getCodonSpecificPosteriorVarianceForCodon . getCodonSpecificQuantilesForCodon . getEstimatedMixtureAssignmentForGene . getEstimatedMixtureAssignmentProbabilitiesForGene . getExpressionEstimates . getGroupList . getLogLikelihoodTrace . getLogPosteriorMean . getLogPosteriorTrace . getMixtureAssignmentEstimate . getNames . getNc . getNcAA . getNoiseOffsetPosteriorMean . getNoiseOffsetVariance . getObservedSynthesisRateSet . getSamples . getSelectionCoefficients . getStdDevSynthesisRatePosteriorMean . getStdDevSynthesisRateVariance . getStepsToAdapt . getSynthesisRate . getSynthesisRatePosteriorMeanForGene . getSynthesisRatePosteriorVarianceForGene . getThinning . getTrace . getTraceObject . initMutationCategories . initSelectionCategories . initializeCovarianceMatrices . initializeGenomeObject . initializeMCMCObject . initializeModelObject . initializeParameterObject . initializeSynthesisRateByGenome . initializeSynthesisRateByList . initializeSynthesisRateByRandom . length.Rcpp_Genome . loadMCMCObject . loadParameterObject . plot.Rcpp_FONSEModel . plot.Rcpp_FONSEParameter . plot.Rcpp_MCMCAlgorithm . plot.Rcpp_ROCModel . plot.Rcpp_ROCParameter . plot.Rcpp_Trace . plotAcceptanceRatios . plotCodonSpecificParameters . readPhiValue . runMCMC . setAdaptiveWidth . setGroupList . setRestartFileSettings . setRestartSettings . setSamples . setStepsToAdapt . setThinning . simulateGenome . summary.Rcpp_Genome . writeMCMCObject . writeParameterObject .
Some associated R codes: RcppExports.R . colorSchemes.R . genomeObject.R . mcmcObject.R . modelObject.R . onloadPackage.R . parameterObject.R . plotMCMCAlgorithmObject.R . plotModelObject.R . plotParameterObject.R . plotTraceObject.R . traceObject.R . Full AnaCoDa package functions and examples
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