| 1 | \name{valse-package} |
| 2 | \alias{valse-package} |
| 3 | \alias{valse} |
| 4 | \docType{package} |
| 5 | |
| 6 | \title{ |
| 7 | \packageTitle{valse} |
| 8 | } |
| 9 | |
| 10 | \description{ |
| 11 | \packageDescription{valse} |
| 12 | } |
| 13 | |
| 14 | \details{ |
| 15 | Two methods are implemented to cluster data with finite mixture |
| 16 | regression models. Those procedures deal with high-dimensional covariates and |
| 17 | responses through a variable selection procedure based on the Lasso estimator. |
| 18 | |
| 19 | The main function is runValse(), which calls all other functions. |
| 20 | See also plot_valse() which plots the relevant parameters after a run. |
| 21 | } |
| 22 | |
| 23 | \author{ |
| 24 | \packageAuthor{valse} |
| 25 | |
| 26 | Maintainer: \packageMaintainer{valse} |
| 27 | } |
| 28 | |
| 29 | %\references{ |
| 30 | % TODO: Literature or other references for background information |
| 31 | %} |
| 32 | |
| 33 | %\examples{ |
| 34 | % TODO: simple examples of the most important functions |
| 35 | %} |