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✨
DESCRIPTION |NEWS
card.svg |card.png
RMixtComp/json (API)

# 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:

On CRAN:

Conda:

clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics

6.64 score 13 stars 28 scripts 308 downloads 5 exports 72 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK302
source / vignettesOK256
linux-release-x86_64OK148
macos-release-arm64OK128
macos-oldrel-arm64OK115
windows-develOK124
windows-releaseOK146
windows-oldrelOK138
wasm-releaseOK153

Exports:extractMixtCompObjectmixtCompLearnmixtCompPredictplotCritslopeHeuristic

Dependencies:askpassbase64encBHbslibcachemclicodetoolscpp11crosstalkcurldata.tabledigestdoParalleldplyrevaluatefarverfastmapfontawesomeforeachfsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrmemoisemimeopensslotelpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownRMixtCompIORMixtCompUtilitiesS7sassscalesstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Data format used in RMixtComp
Parameters | Data | Model | Algo | Available Models | Overview | Details | Gaussian | Weibull | Poisson | NegativeBinomial | Multinomial | Rank_ISR | Func_CS and Func_SharedAlpha_CS | Data Format | Real Data: Gaussian | Real Positive Data: Weibull | Counting Data: Poisson & NegativeBinomial | Categorical Data: Multinomial | Rank Data | Functional Data: Func_CS & Func_SharedAlpha_CS | R Functional Model | Missing Data Summary | (Semi-)Supervised Clustering
Rendered fromdataFormat.Rmdusingknitr::rmarkdown

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

Overview of MixtComp Object
MixtComp Object | warnLog | algo | mixture | variable | type | data | param | MixtCompLearn Object
Rendered frommixtCompObject.Rmdusingknitr::rmarkdown

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

Using RMixtComp with mixed and missing data
Step 1: Data Preparation | Step 2: Learning | Step 3: Interpretation and Visualization | Step 4: Prediction
Rendered fromMixtComp.Rmdusingknitr::rmarkdown

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

Readme and manuals

Help Manual

Help pageTopics
RMixtCompRMixtComp-package RMixtComp
Canadian average annual weather cycleCanadianWeather
Extract a MixtComp objectextractMixtCompObject
Learn and predict using RMixtCompmixtCompLearn mixtCompPredict
Plot of a _MixtCompLearn_ objectplot.MixtCompLearn
Plot BIC and ICLplotCrit
Predict using RMixtComppredict.MixtComp
Print Valuesprint.MixtCompLearn
Prostate Cancer Dataprostate
Simulated Heterogeneous datasimData
Slope heuristicslopeHeuristic
MixtCompLearn Object Summariessummary.MixtCompLearn
Titanic data settitanic