computeDevianceResiduals
                        Deviance residuals of the zero-inflated
                        negative binomial model
computeObservationalWeights
                        Observational weights of the zero-inflated
                        negative binomial model for each entry in the
                        matrix of counts
getAlpha_mu             Returns the matrix of paramters alpha_mu
getAlpha_pi             Returns the matrix of paramters alpha_pi
getBeta_mu              Returns the matrix of paramters beta_mu
getBeta_pi              Returns the matrix of paramters beta_pi
getEpsilon_alpha        Returns the vector of regularization parameter
                        for alpha
getEpsilon_beta_mu      Returns the vector of regularization parameter
                        for beta_mu
getEpsilon_beta_pi      Returns the vector of regularization parameter
                        for beta_pi
getEpsilon_gamma_mu     Returns the vector of regularization parameter
                        for gamma_mu
getEpsilon_gamma_pi     Returns the vector of regularization parameter
                        for gamma_pi
getEpsilon_W            Returns the vector of regularization parameter
                        for W
getEpsilon_zeta         Returns the regularization parameter for the
                        dispersion parameter
getGamma_mu             Returns the matrix of paramters gamma_mu
getGamma_pi             Returns the matrix of paramters gamma_pi
getLogitPi              Returns the matrix of logit of probabilities of
                        zero
getLogMu                Returns the matrix of logarithm of mean
                        parameters
getMu                   Returns the matrix of mean parameters
getPhi                  Returns the vector of dispersion parameters
getPi                   Returns the matrix of probabilities of zero
getTheta                Returns the vector of inverse dispersion
                        parameters
getV_mu                 Returns the gene-level design matrix for mu
getV_pi                 Returns the gene-level design matrix for pi
getW                    Returns the low-dimensional matrix of inferred
                        sample-level covariates W
getX_mu                 Returns the sample-level design matrix for mu
getX_pi                 Returns the sample-level design matrix for pi
getZeta                 Returns the vector of log of inverse dispersion
                        parameters
glmWeightedF            Zero-inflation adjusted statistical tests for
                        assessing differential expression.
imputeZeros             Impute the zeros using the estimated parameters
                        from the ZINB model.
independentFiltering    Perform independent filtering in differential
                        expression analysis.
loglik                  Compute the log-likelihood of a model given
                        some data
nFactors                Generic function that returns the number of
                        latent factors
nFeatures               Generic function that returns the number of
                        features
nParams                 Generic function that returns the total number
                        of parameters of the model
nSamples                Generic function that returns the number of
                        samples
orthogonalizeTraceNorm
                        Orthogonalize matrices to minimize trace norm
                        of their product
penalty                 Compute the penalty of a model
pvalueAdjustment        Perform independent filtering in differential
                        expression analysis.
solveRidgeRegression    Solve ridge regression or logistic regression
                        problems
toydata                 Toy dataset to check the model
zinb.loglik             Log-likelihood of the zero-inflated negative
                        binomial model
zinb.loglik.dispersion
                        Log-likelihood of the zero-inflated negative
                        binomial model, for a fixed dispersion
                        parameter
zinb.loglik.dispersion.gradient
                        Derivative of the log-likelihood of the
                        zero-inflated negative binomial model with
                        respect to the log of the inverse dispersion
                        parameter
zinb.loglik.matrix      Log-likelihood of the zero-inflated negative
                        binomial model for each entry in the matrix of
                        counts
zinb.loglik.regression
                        Penalized log-likelihood of the ZINB regression
                        model
zinb.loglik.regression.gradient
                        Gradient of the penalized log-likelihood of the
                        ZINB regression model
zinb.regression.parseModel
                        Parse ZINB regression model
zinbAIC                 Compute the AIC or BIC of a model given some
                        data
zinbFit                 Fit a ZINB regression model
zinbInitialize          Initialize the parameters of a ZINB regression
                        model
zinbModel               Initialize an object of class ZinbModel
ZinbModel-class         Class ZinbModel
zinbOptimize            Optimize the parameters of a ZINB regression
                        model
zinbOptimizeDispersion
                        Optimize the dispersion parameters of a ZINB
                        regression model
zinbSim                 Simulate counts from a zero-inflated negative
                        binomial model
zinbsurf                Perform dimensionality reduction using a ZINB
                        regression model for large datasets.
zinbwave                Perform dimensionality reduction using a ZINB
                        regression model with gene and cell-level
                        covariates.
