Man pages for drisso/learn2count
Structure Learning for Count Data
| LPGM.ord | Structure learning with Poisson models using the Or-LPGM... |
| nbinom.Simdata | Generate negatibe binomial (NB) data |
| nb.loglik | Log-likelihood of the negative binomial model Given a vector... |
| nb.loglik.dispersion | Log-likelihood of negative binomial model, for a fixed... |
| nb.loglik.regression | log-likelihood of the NB regression model |
| nb.loglik.regression.gradient | Gradient of the log-likelihood of the NB regression model |
| nb.OptimizeDispersion | (NB) model. The NB distribution is parametrized by two... |
| nb.regression.parseModel | Parse ZINB regression model |
| nbscale.noT | Structure learning with negative binomial model using optim |
| nb.wald | Structure learning with negative binomial model using glm |
| PCzinb | Structure learning for count data |
| Poisk2 | Structure learning with Poisson models using the Poisson K2... |
| pois.ord | Structure learning with Poisson models using the Or-PPGM... |
| pois.simdata | Generate Poisson data |
| pois.wald | Structure learning with Poisson models |
| prediction_scores | Prediction scores for estimated graph |
| QPtransform | Quantile matching and power transformation |
| simdata | Generate Poisson, negative binomial (NB), and zero-inflated... |
| zinb0.noT | Structure learning with zero-inflated negative binomial model... |
| zinb1.noT | Structure learning with zero-inflated negative binomial model |
| zinbOptimizeDispersion | (ZINB) model. The ZINB distribution is parametrized by three... |
| zinb.regression.parseModel | Parse ZINB regression model |
| zinb.simdata | Generate zero-inflated negative binomial data |
