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