Package: RMixtComp 4.1.5

Quentin Grimonprez

RMixtComp: Mixture Models with Heterogeneous and (Partially) Missing Data

Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data.

Authors:Vincent Kubicki [aut], Christophe Biernacki [aut], Quentin Grimonprez [aut, cre], Matthieu Marbac-Lourdelle [ctb], Étienne Goffinet [ctb], Serge Iovleff [ctb], Julien Vandaele [ctb]

RMixtComp_4.1.5.tar.gz
RMixtComp_4.1.5.zip(r-4.7)RMixtComp_4.1.5.zip(r-4.6)RMixtComp_4.1.5.zip(r-4.5)
RMixtComp_4.1.5.tgz(r-4.6-any)RMixtComp_4.1.5.tgz(r-4.5-any)
RMixtComp_4.1.5.tar.gz(r-4.7-any)RMixtComp_4.1.5.tar.gz(r-4.6-any)
RMixtComp_4.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RMixtComp/json (API)
NEWS

# Install 'RMixtComp' in R:

install.packages('RMixtComp', repos = c('https://modal-inria.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/modal-inria/mixtcomp/issues

Datasets:

CanadianWeather - Canadian average annual weather cycle
prostate - Prostate Cancer Data
simData - Simulated Heterogeneous data
titanic - Titanic data set

On CRAN:

clustering cpp heterogeneous-data missing-data mixed-data mixture-model statistics

6.42 score 13 stars 17 scripts 295 downloads 5 exports 72 dependencies

Last updated from:d0fc7e870c. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	182
source / vignettes	OK	254
linux-release-x86_64	OK	177
macos-release-arm64	OK	177
macos-oldrel-arm64	OK	162
windows-devel	OK	122
windows-release	OK	143
windows-oldrel	OK	136
wasm-release	OK	137

Exports:extractMixtCompObject mixtCompLearn mixtCompPredict plotCrit slopeHeuristic

Dependencies:askpass base64enc BH bslib cachem cli codetools cpp11 crosstalk curl data.table digest doParallel dplyr evaluate farver fastmap fontawesome foreach fs generics ggplot2 glue gtable highr htmltools htmlwidgets httr isoband iterators jquerylib jsonlite knitr labeling later lazyeval lifecycle magrittr memoise mime openssl otel pillar pkgconfig plotly promises purrr R6 rappdirs RColorBrewer Rcpp RcppEigen rlang rmarkdown RMixtCompIO RMixtCompUtilities S7 sass scales stringi stringr sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml

Data format used in RMixtComp

Quentin Grimonprez

Rendered fromdataFormat.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2023-06-17
Started: 2019-10-14

Overview of MixtComp Object

Quentin Grimonprez

Rendered frommixtCompObject.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2023-06-17
Started: 2019-10-14

Using RMixtComp with mixed and missing data

Quentin Grimonprez

Rendered fromMixtComp.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2023-06-17
Started: 2020-03-20

Help page	Topics
RMixtComp	RMixtComp-package RMixtComp
Canadian average annual weather cycle	CanadianWeather
Extract a MixtComp object	extractMixtCompObject
Learn and predict using RMixtComp	mixtCompLearn mixtCompPredict
Plot of a _MixtCompLearn_ object	plot.MixtCompLearn
Plot BIC and ICL	plotCrit
Predict using RMixtComp	predict.MixtComp
Print Values	print.MixtCompLearn
Prostate Cancer Data	prostate
Simulated Heterogeneous data	simData
Slope heuristic	slopeHeuristic
MixtCompLearn Object Summaries	summary.MixtCompLearn
Titanic data set	titanic

Package: RMixtComp 4.1.5

RMixtComp: Mixture Models with Heterogeneous and (Partially) Missing Data

Data format used in RMixtComp

Overview of MixtComp Object

Using RMixtComp with mixed and missing data

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)