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228ee602 | 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{ | |
859c30ec BA |
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. | |
228ee602 | 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 | %} |