| CNVtools-package | CNVtools : CNV association studies |
| A112 | Copy Number Variant intensity data |
| apply.ldf | Applies a canonical correlation transformation to the data |
| apply.pca | Applies to the data a principal component analysis |
| CNV.fitModel | Fits a mixture of Gaussian to a set of one dimensional points. |
| cnv.plot | Plots posterior probabilty distributions |
| CNVtest.binary | Fits a mixture of Gaussian to CNV data |
| CNVtest.binary.T | CNV association testing using T distributions |
| CNVtest.qt | Fits a mixture of Gaussian to CNV data |
| CNVtest.qt.T | Fits a mixture of Gaussian to CNV data |
| CNVtest.select.model | Select number of components in a CNV |
| CNVtools | CNVtools : CNV association studies |
| compact.data.frame | Compacts the expanded data frame format needed by our fitting procedure into more compact and user friendly version |
| EM.starting.point | Randomly assigns a starting point for the EM algorithm |
| ExpandData | Expands a CNV input data frame for the maximum likelihood routines |
| get.model.spec | Get model specifications (internal function) |
| getparams | Return mixture parameters |
| getQualityScore | Computes a quality score for a CNV fit |
| qt.plot | Makes signal vs trait plots and posterior probabilty distributions |
| test.posterior | Checks posterior probabilities are monotonic. |