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.
Last updated 7 months ago
clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
6.49 score 13 stars 12 scripts 281 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.
Last updated 7 months ago
clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
5.29 score 13 stars 1 packages 2 scripts 302 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.
Last updated 2 years ago
clusteringhacktoberfestrank
5.05 score 1 stars 1 packages 37 scripts 444 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.
Last updated 5 days ago
categorical-datafunctional-data-analysishacktoberfest
4.18 score 3 stars 3 scripts 330 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>).
Last updated 2 years ago
group-lassovariable-selection
3.61 score 3 stars 27 scripts 216 downloads