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Package breakpoint July Type Package Title An R Package for Multiple BreakPoint Detection via the CrossEntropy Method Version

0 Date 20140114 Author Priyadarshana WJRM and Georgy Sofronov Maintainer Priyadarshana WJRM Description Implements the crossentropy CE method which is a model based stochastic optimization tech nique to estimate both the number as well as the corresp

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Package breakpoint July Type Package Title An R Package for Multiple BreakPoint Detection via the CrossEntropy Method Version






Presentation on theme: "Package breakpoint July Type Package Title An R Package for Multiple BreakPoint Detection via the CrossEntropy Method Version"— Presentation transcript:

2breakpoint-packageIndex25 breakpoint-packageMultipleBreak-PointDetectionviatheCross-EntropyMethod DescriptionThebreakpointpackageimplementsvariantsoftheCross-Entropy(CE)methodproposedinPriyadar-shanaandSofronov(2015,2012aand2012b)toestimateboththenumberandthecorrespondinglocationsofbreak-pointsinbiologicalsequencesofcontinuousanddiscretemeasurements.Theproposedmethodprimarilybuilttodetectmultiplebreak-pointsingenomicsequences.However,itcanbeeasilyextendedandappliedtootherproblems.DetailsPackage:breakpointType:PackageVersion:1.2Date:2016-01-11License:GPL2.0"breakpoint""packageprovidesestimatesonboththenumberaswellasthecorrespondinglocationsofbreak-points.ThealgorithmsutilizetheCross-Entropy(CE)method,whichisamodel-basedstochasticoptimizationproceduretoobtaintheestimatesonlocations.Modelselectionproceduresareusedtoobtainthenumberofbreak-points.Currentimplementationofthemethodologyworksasanexactsearchmethodinestimatingthenumberofbreak-points.However,itsupportscalculationsiftheinitiallocationsareprovided.Aparallelimplementationoftheprocedurescanbecarried-outinUnix/Linux/MACOSXandWINDOWSOSwiththeuseof"parallel"and"doParallel"packages.Author(s)Priyadarshana,W.J.R.M.andSofronov,G.Maintainer:Priyadarshana,W.J.R.M.w&#xmjay;ꅐardana@swin.edu.auReferencesPriyadarshana,W.J.R.M.,SofronovG.(2015).MultipleBreak-PointsDetectioninArrayCGHDataviatheCross-EntropyMethod,IEEE/ACMTransactionsonComputationalBiologyandBioin-formatics,12(2),pp.487-498.Priyadarshana,W.J.R.M.andSofronov,G.(2012a).AModiedCross-EntropyMethodforDetectingMultipleChange-PointsinDNACountData.InProc.oftheIEEEConferenceonEvo-lutionaryComputation(CEC),1020-1027,DOI:10.1109/CEC.2012.6256470.Priyadarshana,W.J.R.M.andSofronov,G.(2012b).TheCross-EntropyMethodandMultipleChange-PointsDetectioninZero-InatedDNAreadcountdata.In:Y.T.Gu,S.C.Saha(Eds.)The 4CE.NBpenaltyUsercanselecteitherBICorAICtoobtainthenumberofbreak-points.Options:"BIC","AIC".Defaultis"BIC".parallelAlogicalargumentspecifyingifparallelcomputationshouldbecarried-out(TRUE)ornot(FALSE).Bydefaultitissetas`FALSE'.InWINDOWSOSsystems"snow"functionalitiesareused,whereasinUnix/Linux/MACOSX"multicore"functionalitiesareusedtocarryoutparallelcomputationswiththemaximumnumberofcoresavailable.DetailsThenegativebinomial(NB)distributionisusedtomodelthediscrete(count)data.NBmodelispre-ferredoverthePoissionmodelwhenover-dispersionisobservedinthecountdata.Aperformancefunctionscore(BICorAIC)iscalculatedforeachofthesolutionsgeneratedbythestatisticaldistri-bution(fourparameterbetadistributionortruncatednormaldistribution),whichisusedtosimulatebreak-pointsfromnobreak-pointtotheuserprovidedmaximumnumberofbreak-points(defaultis10).ThesolutionthatminimizestheBIC/AICwithrespecttothenumberofbreak-pointsisre-portedastheoptimalsolution.Finally,alistcontainingavectorofbreak-pointlocations,numberofbreak-points,BIC/AICvaluesandlog-likelihoodvalueisreturnedintheconsole.ValueAlistisreturnedwithfollowingitems:No.BPsThenumberofbreak-pointsinthedatathatisestimatedbytheCEmethodBP.LocAvectorofbreak-pointlocationsBIC/AICBIC/AICvaluellLoglikelihoodoftheoptimalsolutionAuthor(s)Priyadarshana,W.J.R.M.w&#xmjay;ꅐardana@swin.edu.auReferencesPriyadarshana,W.J.R.M.andSofronov,G.(2012a)AModiedCross-EntropyMethodforDetect-ingMultipleChange-PointsinDNACountData,InProc.oftheIEEEConferenceonEvolutionaryComputation(CEC),1020-1027,DOI:10.1109/CEC.2012.6256470.Priyadarshana,W.J.R.M.andSofronov,G.(2012b)TheCross-EntropyMethodandMultipleChange-PointsDetectioninZero-InatedDNAreadcountdata,In:Y.T.Gu,S.C.Saha(Eds.)The4thInternationalConferenceonComputationalMethods(ICCM2012),1-8,ISBN978-1-921897-54-2.Rubinstein,R.,andKroese,D.(2004)TheCross-EntropyMethod:AUniedApproachtoCom-binatorialOptimization,Monte-CarloSimulationandMachineLearning.Springer-Verlag,NewYork.Schwarz,G.(1978)Estimatingthedimensionofamodel,TheAnnalsofStatistics,6(2),461-464. 6CE.NB.InitUsageCE.NB.Init(data,init.locs,eps=0.01,rho=0.05,M=200,h=5,a=0.8,b=0.8,distyp=1,penalty="BIC",var.init=1e+05,parallel=FALSE)Argumentsdatadatatobeanalysed.Asinglecolumnarrayoradataframe.init.locsInitialbreak-pointlocations.epsthecut-offvalueforthestoppingcriterionintheCEmethod.Defaultvalueis0.01.rhothefractionwhichisusedtoobtainthebestperformingsetofsamplesolutions(i.e.,elitesample).Defaultvalueis0.05.Msamplesizetobeusedinsimulatingthelocationsofbreak-points.Defaultvalueis200.hminimumaberrationwidth.Defaultis5.aasmoothingparametervalue.Itisusedinthefourparameterbetadistributiontosmoothbothshapeparameters.Whensimulatingfromthetruncatednormaldis-tribution,thisvalueisusedtosmooththeestimatesofthemeanvalues.Defaultis0.8.basmoothingparametervalue.Itisusedinthetruncatednormaldistributiontosmooththeestimatesofthestandarddeviation.Defaultis0.8.distypdistributiontosimulatebreak-pointlocations.Options:1=fourparameterbetadistribution,2=truncatednormaldistribution.Defaultis1.penaltyUsercanselecteitherBICorAICtoobtainthenumberofbreak-points.Options:"BIC","AIC".Defaultis"BIC".var.initInitialvariancevaluetofacilitatethesearchprocess.Defaultis100000.parallelAlogicalargumentspecifyingifparallelcomputationshouldbecarried-out(TRUE)ornot(FALSE).Bydefaultitissetas`FALSE'.InWINDOWSOSsystems"snow"functionalitiesareused,whereasinUnix/Linux/MACOSX"multicore"functionalitiesareusedtocarryoutparallelcomputationswiththemaximumnumberofcoresavailable.DetailsThenegativebinomial(NB)distributionisusedtomodelthediscrete(count)data.NBmodelispre-ferredoverthePoissionmodelwhenover-dispersionisobservedinthecountdata.Aperformancefunctionscore(BICorAIC)iscalculatedforeachofthesolutionsgeneratedbythestatisticaldis-tribution(fourparameterbetadistributionortruncatednormaldistribution)withrespecttotheuserprovidedinitiallocations.Finally,alistcontainingavectorofbreak-pointlocations,numberofbreak-points,BIC/AICvaluesandlog-likelihoodvalueisreturnedintheconsole.ValueAlistisreturnedwithfollowingitems:No.BPsThenumberofbreak-points CE.Normal.Init.Mean9aasmoothingparametervalue.Itisusedinthefourparameterbetadistributiontosmoothbothshapeparameters.Whensimulatingfromthetruncatednormaldis-tribution,thisvalueisusedtosmooththeestimatesofthemeanvalues.Defaultis0.8.basmoothingparametervalue.Itisusedinthetruncatednormaldistributiontosmooththeestimatesofthestandarddeviation.Defaultis0.8.distypdistributiontosimulatebreak-pointlocations.Options:1=fourparameterbetadistribution,2=truncatednormaldistribution.Defaultis1.penaltyUsercanselectfrommBIC,BICorAICtoobtaintheoptimalnumberofbreak-points.Options:"mBIC","BIC"and"AIC".Defaultis"mBIC".var.initInitialvariancevaluetofacilitatethesearchprocess.Defaultis100000.parallelAlogicalargumentspecifyingifparallelcomputationshouldbecarried-out(TRUE)ornot(FALSE).Bydefaultitissetas`FALSE'.InWINDOWSOSsystems"snow"functionalitiesareused,whereasinUnix/Linux/MACOSX"multicore"functionalitiesareusedtocarryoutparallelcomputationswiththemaximumnumberofcoresavailable.DetailsThenormaldistributionisusedtomodelthecontinuousdata.Aperformancefunctionscore(mBIC/BIC/AIC)iscalculatedforeachofthesolutionsgeneratedbythestatisticaldistribution(fourparameterbetadistributionortruncatednormaldistribution),whichisusedtosimulatebreak-pointsfromtheuserprovidedinitiallocations.Thesolutionthatmaximizestheselectioncriteriawithre-specttothenumberofbreak-pointsisreportedastheoptimalsolution.Finally,alistcontainingavectorofbreak-pointlocations,numberofbreak-points,mBIC/BIC/AICvaluesandlog-likelihoodvalueisreturnedintheconsole.ValueAlistisreturnedwithfollowingitems:No.BPsThenumberofbreak-pointsBP.LocAvectorofbreak-pointlocationsmBIC/BIC/AICmBIC/BIC/AICvaluellLoglikelihoodoftheoptimalsolutionAuthor(s)Priyadarshana,W.J.R.M.w&#xmjay;ꅐardana@swin.edu.auReferencesPriyadarshana,W.J.R.M.,SofronovG.(2015).MultipleBreak-PointsDetectioninArrayCGHDataviatheCross-EntropyMethod,IEEE/ACMTransactionsonComputationalBiologyandBioin-formatics,12(2),pp.487-498.Priyadarshana,W.J.R.M.andSofronov,G.(2012)AModiedCross-EntropyMethodforDetect-ingMultipleChange-PointsinDNACountData,InProc.oftheIEEEConferenceonEvolutionaryComputation(CEC),1020-1027,DOI:10.1109/CEC.2012.6256470. CE.Normal.Init.MeanVar11Argumentsdatadatatobeanalysed.Asinglecolumnarrayoradataframe.init.locsInitialbreak-pointlocations.epsthecut-offvalueforthestoppingcriterionintheCEmethod.Defaultvalueis0.01.rhothefractionwhichisusedtoobtainthebestperformingsetofsamplesolutions(i.e.,elitesample).Defaultvalueis0.05.Msamplesizetobeusedinsimulatingthelocationsofbreak-points.Defaultvalueis200.hminimumaberrationwidth.Defaultis5.aasmoothingparametervalue.Itisusedinthefourparameterbetadistributiontosmoothbothshapeparameters.Whensimulatingfromthetruncatednormaldis-tribution,thisvalueisusedtosmooththeestimatesofthemeanvalues.Defaultis0.8.basmoothingparametervalue.Itisusedinthetruncatednormaldistributiontosmooththeestimatesofthestandarddeviation.Defaultis0.8.distypdistributiontosimulatebreak-pointlocations.Options:1=fourparameterbetadistribution,2=truncatednormaldistribution.Defaultis1.penaltyUsercanselecteitherfromBICorAICtoobtaintheoptimalnumberofbreak-points.Options:"BIC"and"AIC".Defaultis"BIC".var.initInitialvariancevaluetofacilitatethesearchprocess.Defaultis100000.parallelAlogicalargumentspecifyingifparallelcomputationshouldbecarried-out(TRUE)ornot(FALSE).Bydefaultitissetas`FALSE'.InWINDOWSOSsystems"snow"functionalitiesareused,whereasinUnix/Linux/MACOSX"multicore"functionalitiesareusedtocarryoutparallelcomputationswiththemaximumnumberofcoresavailable.DetailsThenormaldistributionisusedtomodelthecontinuousdata.Aperformancefunctionscore(BIC/AIC)iscalculatedforeachofthesolutionsgeneratedbythestatisticaldistribution(fourpa-rameterbetadistributionortruncatednormaldistribution),whichisusedtosimulatebreak-pointsfromtheuserprovidedinitiallocations.Changesinbothmeanandvariancesareestimated.Thesolutionthatmaximizestheselectioncriteriawithrespecttothenumberofbreak-pointsisreportedastheoptimalsolution.Finally,alistcontainingavectorofbreak-pointlocations,numberofbreak-points,BIC/AICvaluesandlog-likelihoodvalueisreturnedintheconsole.ValueAlistisreturnedwithfollowingitems:No.BPsThenumberofbreak-pointsBP.LocAvectorofbreak-pointlocationsBIC/AICBIC/AICvaluellLoglikelihoodoftheoptimalsolution CE.Normal.Mean13 CE.Normal.MeanMultipleBreak-pointDetectionviatheCEMethodforContinuousData(Meanlevels) DescriptionThisfunctionperformscalculationstoestimateboththenumberofbreak-pointsandtheircorre-spondinglocationsofcontinuousmeasurementswiththeCEmethod.Thenormaldistributionisusedtomodeltheobservedcontinousdata.Accrossthesegmentsstandarddeviationisassumedtobethesame.Thisfunctionsupportsforthesimulationofbreak-pointlocationsbasedonthefourparameterbetadistributionortruncatednormaldistribution.UsercanselectfromthemodiedBIC(mBIC)proposedbyZhangandSiegmund(2007),BICorAICtoobtaintheoptimalnumberofbreak-points.UsageCE.Normal.Mean(data,Nmax=10,eps=0.01,rho=0.05,M=200,h=5,a=0.8,b=0.8,distyp=1,penalty="mBIC",parallel=FALSE)Argumentsdatadatatobeanalysed.Asinglecolumnarrayoradataframe.Nmaxmaximumnumberofbreak-points.Defaultvalueis10.epsthecut-offvalueforthestoppingcriterionintheCEmethod.Defaultvalueis0.01.rhothefractionwhichisusedtoobtainthebestperformingsetofsamplesolutions(i.e.,elitesample).Defaultvalueis0.05.Msamplesizetobeusedinsimulatingthelocationsofbreak-points.Defaultvalueis200.hminimumaberrationwidth.Defaultis5.aasmoothingparametervalue.Itisusedinthefourparameterbetadistributiontosmoothbothshapeparameters.Whensimulatingfromthetruncatednormaldis-tribution,thisvalueisusedtosmooththeestimatesofthemeanvalues.Defaultis0.8.basmoothingparametervalue.Itisusedinthetruncatednormaldistributiontosmooththeestimatesofthestandarddeviation.Defaultis0.8.distypdistributionstosimulatebreak-pointlocations.Options:1=fourparameterbetadistribution,2=truncatednormaldistribution.Defaultis1.penaltyUsercanselectfrommBIC,BICorAICtoobtaintheoptimalnumberofbreak-points.Options:"mBIC","BIC"and"AIC".Defaultis"mBIC".parallelAlogicalargumentspecifyingifparallelcomputationshouldbecarried-out(TRUE)ornot(FALSE).Bydefaultitissetas`FALSE'.InWINDOWSOSsystems"snow"functionalitiesareused,whereasinUnix/Linux/MACOSX"multicore"functionalitiesareusedtocarryoutparallelcomputationswiththemaximumnumberofcoresavailable. CE.Normal.MeanVar15Examplesdata(ch1.GM03563)##Notrun:##CEwithfourparameterbetadistributionwithmBICastheselectioncriterion##obj1CE.Normal.Mean(ch1.GM03563,distyp=1,penalty="mBIC",parallel=TRUE)profilePlot(obj1,simdata)##CEwithtruncatednormaldistributionwithmBICastheselectioncriterion##obj2CE.Normal.Mean(ch1.GM03563,distyp=2,penalty="mBIC",parallel=TRUE)profilePlot(obj2,simdata)##End(Notrun) CE.Normal.MeanVarMultiplebreak-pointdetectionviatheCEmethodforcontinuousdata(bothmeanandvariancechanges) DescriptionThisfunctionperformscalculationstoestimateboththenumberofbreak-pointsandtheircorre-spondinglocationsofcontinuousmeasurementswiththeCEmethod.Thenormaldistributionisusedtomodeltheobservedcontinousdata.Thisfunctionsupportsforthesimulationofbreak-pointlocationsbasedonthefourparameterbetadistributionortruncatednormaldistribution.UsercanselecteitherfromthegenralBICorAICtoobtaintheoptimalnumberofbreak-points.UsageCE.Normal.MeanVar(data,Nmax=10,eps=0.01,rho=0.05,M=200,h=5,a=0.8,b=0.8,distyp=1,penalty="BIC",parallel=FALSE)Argumentsdatadatatobeanalysed.Asinglecolumnarrayoradataframe.Nmaxmaximumnumberofbreak-points.Defaultvalueis10.epsthecut-offvalueforthestoppingcriterionintheCEmethod.Defaultvalueis0.01.rhothefractionwhichisusedtoobtainthebestperformingsetofsamplesolutions(i.e.,elitesample).Defaultvalueis0.05.Msamplesizetobeusedinsimulatingthelocationsofbreak-points.Defaultvalueis200.hminimumaberrationwidth.Defaultis5.aasmoothingparametervalue.Itisusedinthefourparameterbetadistributiontosmoothbothshapeparameters.Whensimulatingfromthetruncatednormaldis-tribution,thisvalueisusedtosmooththeestimatesofthemeanvalues.Defaultis0.8. CE.ZINB17Zhang,N.R.,andSiegmund,D.O.(2007)AmodiedBayesinformationcriterionwithapplicationstotheanalysisofcomparativegenomichybridizationdata.Biometrics,63,22-32.SeeAlsoCE.Normal.Init.MeanforCEwithnormalwithinitiallocations,CE.Normal.MeanforCEwithnormaltodetectbreak-pointsinmeanlevels,CE.Normal.Init.MeanVarforCEwithnormaltodetectbreak-pointsinbothmeanandvariancewithinitiallocations,profilePlottoobtainmeanproleplot.Examples##Notrun:simdataas.data.frame(c(rnorm(200,100,5),rnorm(1000,160,8),rnorm(300,120,10)))##CEwithfourparameterbetadistributionwithBICastheselectioncriterion##obj1CE.Normal.MeanVar(simdata,distyp=1,penalty="BIC",parallel=TRUE)profilePlot(obj1,simdata)##CEwithtruncatednormaldistributionwithBICastheselectioncriterion##obj2CE.Normal.MeanVar(simdata,distyp=2,penalty="BIC",parallel=TRUE)profilePlot(obj2,simdata)##End(Notrun) CE.ZINBMultipleBreak-pointDetectionviatheCEMethodwithZero-InatedNegativeBinomialDistribution DescriptionPerformscalculationstoestimateboththenumberofbreak-pointsandtheircorrespondinglocationsofdiscretemeasurementswiththeCEmethod.Zero-inatednegativebinomialdistributionisusedtomodeltheexcesszeroobservationsandtomodelover-dispersesionintheoberveddiscrete(count)data.Thisfunctionsupportsforthesimulationofbreak-pointlocationsintheCEalgorithmbasedonthefourparameterbetadistributionandtruncatednormaldistribution.ThegeneralBICorAICcanbeusedtoselecttheoptimalnumberofbreak-points.UsageCE.ZINB(data,Nmax=10,eps=0.01,rho=0.05,M=200,h=5,a=0.8,b=0.8,distyp=1,penalty="BIC",parallel=FALSE) 20CE.ZINB.Initobj1profilePlot(obj1,simdata)#Toobtainthemeanprofileplot##CEwithtruncatednormaldistributionwithBICastheselectioncriterion##obj2CE.ZINB(simdata,distyp=2,penalty=BIC,parallel=TRUE)#Parallelcomputationobj2profilePlot(obj2,simdata)#Toobtainthemeanprofileplot##End(Notrun) CE.ZINB.InitMultipleBreak-pointDetectionviatheCEMethodwithZero-InatedNegativeBinomialDistributionwithinitiallocations DescriptionPerformscalculationstoestimatethebreak-pointlocationswhentheirinitialvaluesaregiven.Zero-inatednegativebinomialdistributionisusedtomodeltheexcesszeroobservationsandtomodelover-dispersesionintheoberveddiscrete(count)data.Thisfunctionsupportsforthesimulationofbreak-pointlocationsintheCEalgorithmbasedonthefourparameterbetadistributionandtruncatednormaldistribution.ThegeneralBICorAICcanbeusedtoselecttheoptimalnumberofbreak-points.UsageCE.ZINB.Init(data,init.locs,eps=0.01,rho=0.05,M=200,h=5,a=0.8,b=0.8,distyp=1,penalty="BIC",var.init=1e+05,parallel=FALSE)Argumentsdatadatatobeanalysed.Asinglecolumnarrayoradataframe.init.locsInitialbreak-pointlocations.epsthecut-offvalueforthestoppingcriterionintheCEmethod.Defaultvalueis0.01.rhothefractionwhichisusedtoobtainthebestperformingsetofsamplesolutions(i.e.,elitesample).Defaultvalueis0.05.Msamplesizetobeusedinsimulatingthelocationsofbreak-points.Defaultvalueis200.hminimumaberrationwidth.Defaultis5.aasmoothingparametervalue.Itisusedinthefourparameterbetadistributiontosmoothbothshapeparameters.Whensimulatingfromthetruncatednormaldis-tribution,thisvalueisusedtosmooththeestimatesofthemeanvalues.Defaultis0.8. 22CE.ZINB.InitSchwarz,G.(1978)Estimatingthedimensionofamodel,TheAnnalsofStatistics,6(2),461-464.SeeAlsoCE.NBforCEwithnegativebinomial,CE.NB.InitforCEwithnegativebinomialwithinitiallocations,CE.ZINBforCEwithzero-inatednegativebinomial,profilePlottoobtainmeanproleplot.Examples####Simulateddataexample####gamlssRpackageisusedtosimulatedatafromtheZINB.##Notrun:library(gamlss)segs6#NumberofsegementsMc(1500,2200,800,2500,1000,2000)#Segmentwidth#true.locationsc(1501,3701,4501,7001,8001)#Truebreak-pointlocationssegNULLpc(0.6,0.1,0.3,0.05,0.2,0.4)#Specificationofp'soneachsegment'sigma.valc(1,2,3,4,5,6)#Specificationofsigmavlauesfor(jin1:segs){segc(seg,rZINBI(M[j],mu=300,sigma=sigma.val[j],nu=p[j]))}simdataas.data.frame(seg)rm(p,M,seg,segs,j,sigma.val)#plot(data[,1])##CEwiththefourparameterbetadistributionwithBICastheselectioncriterion##init.locic(1400,3400,4650,7100,8200)obj1CE.ZINB.Init(simdata,init.locs=init.loci,distyp=1,penalty=BIC,parallel=TRUE)obj1profilePlot(obj1,simdata)#Toobtainthemeanprofileplot##CEwithtruncatednormaldistributionwithBICastheselectioncriterion##obj2CE.ZINB.Init(simdata,init.locs=init.loci,distyp=2,penalty=BIC,parallel=TRUE)obj2profilePlot(obj2,simdata)#Toobtainthemeanprofileplot##End(Notrun) 24prolePlot profilePlotMeanproleplot DescriptionPlottingfunctiontoobtainmeanproleplotofthetestingdatasetbasedontheestimatesofthebreak-points.AnRobjectcreatedfromtheCE.Normal,CE.NBotCE.ZINBisrequired.Usercanaltertheaxisnames.UsageprofilePlot(obj,data,x.label="DataSequence",y.label="Value")ArgumentsobjRobjectcreatedfromCE.Normal,CE.NBorCE.ZINB.datadatatobeanalysed.Asinglecolumnarrayoradataframe.x.labelxaxislabel.Defaultis"DataSequence".y.labelyaxislabel.Defaultis"Value".Author(s)Priyadarshana,W.J.R.M.w&#xmjay;ꅐardana@swin.edu.auSeeAlsoCE.Normal.Mean,CE.NB,CE.ZINB.Examplesdata(ch1.GM03563)##Notrun:##CEwithfourparameterbetadistribution##obj1CE.Normal.Mean(ch1.GM03563,distyp=1,penalty="mBIC",parallel=TRUE)profilePlot(obj1)##CEwithtruncatednormaldistribution##obj2CE.Normal.Mean(ch1.GM03563,distyp=2,penalty="mBIC",parallel=TRUE)profilePlot(obj2)##End(Notrun)