$,LineagePulseObject-method
                        List-like accessor methods for
                        LineagePulseObject: $
LPsetters               LineagePulseObject setters
LineagePulseObject-class
                        Container class for LineagePulse output
[[,LineagePulseObject,character,missing-method
                        List-like accessor methods for
                        LineagePulseObject: names()
accessors               LineagePulseObject accession methods
calcNormConst           Compute size factors for a LineagePulse-object
calcPostDrop_Matrix     Calculate posterior of drop-out
calcPostDrop_Vector     Calculate posterior of drop-out
decompressDispByGene    Compute dispersion parameter estimates from
                        mean parameter model for a gene
decompressDispByGeneMM
                        Compute mean parameter estimate matrix from
                        mean parameter model for a gene (strDispModel
                        == "MM")
decompressDropoutRateByCell
                        Compute dropout rate parameter estimates from
                        dropout rate model for a cell
decompressDropoutRateByGene
                        Compute dropout rate parameter estimates from
                        dropout rate model for a gene
decompressMeansByGene   Compute mean parameter estimates from mean
                        parameter model for a gene
decompressMuByGeneMM    Compute mean parameter estimate matrix from
                        mean parameter model for a gene (strMuModel ==
                        "MM")
evalDropoutModel        Compute value of logistic dropout function
                        given with scale boundaries
evalDropoutModel_comp   Compiled function: evalDropoutModel
evalImpulseModel        Compute value of impulse function given
                        parameters.
evalImpulseModel_comp   Compiled function: evalImpulseModel
evalLogLikGene          Wrapper for log likelihood of (zero-inflated)
                        negative binomial model for a vector of counts.
evalLogLikGeneMM        Wrapper for log likelihood of zero-inflated
                        negative binomial model for a vector of counts.
evalLogLikMatrix        Wrapper for log likelihood of (zero-inflated)
                        negative binomial model for a matrix of counts
                        (parallelised).
evalLogLikMuDispGeneFit
                        Cost function (zero-inflated) negative binomial
                        model for mean and dispersion model fitting
evalLogLikMuDispGeneFit_comp
                        Compiled function: evalLogLikMuDispGeneFit
evalLogLikNB            Compute loglikelihood of negative binomial
                        model for a vector of counts.
evalLogLikNB_comp       Compiled function: evalLogLikNB
evalLogLikPiZINB_ManyCells
                        Cost function zero-inflated negative binomial
                        model for drop-out fitting for many cells
evalLogLikPiZINB_ManyCells_comp
                        Cost function zero-inflated negative binomial
                        model for drop-out fitting
evalLogLikPiZINB_SingleCell
                        Cost function zero-inflated negative binomial
                        model for drop-out fitting
evalLogLikPiZINB_SingleCell_comp
                        Cost function zero-inflated negative binomial
                        model for drop-out fitting
evalLogLikZINB          Compute loglikelihood of zero-inflated negative
                        binomial model for a vector of counts.
evalLogLikZINB_comp     Compiled function: evalLogLikZINB
fitLPModels             Fit all models necessary for LineagePulse
fitModel                Fit (zero-inflated) negative binomial model to
                        data
fitMuDisp               Coordinate mean and dispersion parameter
                        co-estimation step
fitMuDispGene           Optim wrapper for gene-wise models other than
                        mixture model.
fitMuDispGeneImpulse    Multiple initilalisation wrapper for impulse
                        mean model
fitMuDispGeneMM         Optim wrapper for gene-wise models other than
                        mixture model.
fitPi                   Global wrapper for fitting of all drop-out
                        models
fitPi_ManyCells         Optim wrapper for drop-out model fitting on
                        many cells
fitPi_SingleCell        Optim wrapper for drop-out model fitting on
                        single cell
getFitsDispersion       Get dispersion model fits
getFitsDropout          Get drop-out model fits
getFitsMean             Get mean model fits
getNormData             Return depth and batch corrected data
getPostDrop             Get posteriors of drop-out
initDispModel           Initialise dispersion model container object
initDropModel           Initialise drop-out model container object
initMuModel             Initialise mean model container object
initialiseImpulseParameters
                        Estimate impulse model parameter
                        initialisations
names,LineagePulseObject-method
                        List-like accessor methods for
                        LineagePulseObject: names()
plotCellDensity         Plot density of cells in continuous covariate
plotGene                Plot counts and model for one gene
processSCData           Prepare single cell data for analysis
runDEAnalysis           Differential expression analysis
runLineagePulse         LineagePulse wrapper: Differential expression
                        analysis on scRNA-seq
simulateContinuousDataSet
                        Simulate a data set for LinagePulse Simulates a
                        data set with genes with constant and impulse
                        expression traces. Expression strength and
                        variation in impulse like traces are
                        parameterised and random. All temporary files
                        are saved into dirOutSimulation and only the
                        objects necessary for running LineagePulse (the
                        count matrix and the continuous covariate
                        vector are returned). The remaining objects
                        representing hidden parameters can be used to
                        evaluate parameter estimates. Cells are
                        distributed uniformly in the continuous
                        covariate.
sortGeneTrajectories    Cluster expression mean trajectories
testDropout             Test for existance of drop-out with
                        log-likelihood ratio test
writeReport             Print LineagePulse report
