Negative binomial model for scRNA-seq

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Documentation for package ‘NewWave’ version 1.19.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

newAIC	Compute the AIC of a model given some data
newAIC-method	Compute the AIC of a model given some data
newAlpha	Returns the matrix of paramters alpha
newAlpha-method	Class newmodel
newBeta	Returns the matrix of paramters beta
newBeta-method	Class newmodel
newBIC	Compute the BIC of a model given some data
newBIC-method	Compute the BIC of a model given some data
newEpsilon_alpha	Returns the vector of regularization parameter for alpha
newEpsilon_alpha-method	Class newmodel
newEpsilon_beta	Returns the vector of regularization parameter for beta
newEpsilon_beta-method	Class newmodel
newEpsilon_gamma	Returns the vector of regularization parameter for gamma
newEpsilon_gamma-method	Class newmodel
newEpsilon_W	Returns the vector of regularization parameter for W
newEpsilon_W-method	Class newmodel
newEpsilon_zeta	Returns the regularization parameter for the dispersion parameter
newEpsilon_zeta-method	Class newmodel
newFit	Fit a nb regression model
newFit-method	Fit a nb regression model
newGamma	Returns the matrix of paramters gamma
newGamma-method	Class newmodel
newloglik	Compute the log-likelihood of a model given some data
newloglik-method	Compute the log-likelihood of a model given some data
newLogMu	Returns the matrix of logarithm of mean parameters
newLogMu-method	Class newmodel
newmodel	Initialize an object of class newmodel
newmodel-class	Class newmodel
newMu	Returns the matrix of mean parameters
newMu-method	Class newmodel
newpenalty	Compute the penalty of a model
newpenalty-method	Compute the penalty of a model
newPhi	Returns the vector of dispersion parameters
newPhi-method	Class newmodel
newSim	Simulate counts from a negative binomial model
newSim-method	Simulate counts from a negative binomial model
newTheta	Returns the vector of inverse dispersion parameters
newTheta-method	Class newmodel
newV	Returns the gene-level design matrix for mu
newV-method	Class newmodel
newW	Returns the low-dimensional matrix of inferred sample-level covariates W
newW-method	Class newmodel
newWave	Perform dimensionality reduction using a nb regression model with gene and cell-level covariates.
newWave-method	Perform dimensionality reduction using a nb regression model with gene and cell-level covariates.
newX	Returns the sample-level design matrix for mu
newX-method	Class newmodel
newZeta	Returns the vector of log of inverse dispersion parameters
newZeta-method	Class newmodel
numberFactors	Generic function that returns the number of latent factors
numberFactors-method	Class newmodel
numberFeatures	Generic function that returns the number of features
numberFeatures-method	Class newmodel
numberParams	Generic function that returns the total number of parameters of the model
numberParams-method	Generic function that returns the total number of parameters of the model
numberSamples	Generic function that returns the number of samples
numberSamples-method	Class newmodel
show-method	Class newmodel