VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type Data
Set with Missing Values
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
| Version: |
2.1.3.1 |
| Depends: |
R (≥ 3.3) |
| Imports: |
methods, Rcpp (≥ 0.11.1), parallel, mgcv, ggplot2, shiny |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, dplyr, htmltools, scales, plyr |
| Published: |
2020-10-14 |
| DOI: |
10.32614/CRAN.package.VarSelLCM |
| Author: |
Matthieu Marbac and Mohammed Sedki |
| Maintainer: |
Mohammed Sedki <mohammed.sedki at u-psud.fr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
http://varsellcm.r-forge.r-project.org/ |
| NeedsCompilation: |
yes |
| Citation: |
VarSelLCM citation info |
| Materials: |
NEWS |
| In views: |
Cluster, MissingData |
| CRAN checks: |
VarSelLCM results |
Documentation:
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