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