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.
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clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
6.42 score 13 stars 17 scripts 295 downloadsRMixtCompUtilities - Utility Functions for 'MixtComp' Outputs
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility functions to facilitate the analysis of 'MixtComp' output.
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clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
5.19 score 13 stars 1 dependents 2 scripts 287 downloadscfda - Categorical Functional Data Analysis
Package for the analysis of categorical functional data. The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>. It also provides functions to perform basic statistical analysis on categorical functional data.
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categorical-datafunctional-data-analysishacktoberfest
5.19 score 4 stars 1 dependents 26 scripts 319 downloadsRankcluster - Model-Based Clustering for Multivariate Partial Ranking Data
Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). Multivariate rankings as well as partial rankings are taken into account. This algorithm is based on an extension of the Insertion Sorting Rank (ISR) model for ranking data, which is a meaningful and effective model parametrized by a position parameter (the modal ranking, quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity of the rank population is modelled by a mixture of ISR, whereas conditional independence assumption is considered for multivariate rankings.
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4.58 score 1 stars 38 scripts 216 downloadsMLGL - Multi-Layer Group-Lasso
It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).
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group-lassovariable-selection
3.61 score 3 stars 27 scripts 222 downloads