Showing 7 of total 7 results (show query)
cran
flexmix:Flexible Mixture Modeling
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.
Maintained by Bettina Gruen. Last updated 1 months ago.
5 stars 8.42 score 113 dependents
thothorn
modeltools:Tools and Classes for Statistical Models
A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin' and `party' have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome!
Maintained by Torsten Hothorn. Last updated 5 years ago.
6.56 score 84 scripts 254 dependents
comeetie
greed:Clustering and Model Selection with the Integrated Classification Likelihood
An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).
Maintained by Etienne Côme. Last updated 2 years ago.
14 stars 5.94 score 41 scripts
bioc
coseq:Co-Expression Analysis of Sequencing Data
Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided.
Maintained by Andrea Rau. Last updated 5 months ago.
geneexpressionrnaseqsequencingsoftwareimmunooncology
4.98 score 16 scripts
r-forge
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.
Maintained by Mohammed Sedki. Last updated 6 years ago.
4.95 score 49 scripts 2 dependentsbioc
flowMerge:Cluster Merging for Flow Cytometry Data
Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed.
Maintained by Greg Finak. Last updated 5 months ago.
immunooncologyclusteringflowcytometry
4.56 score 6 scripts 1 dependents
nagodem
rebmix:Finite Mixture Modeling, Clustering & Classification
Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
Maintained by Marko Nagode. Last updated 9 months ago.
1 stars 2.66 score 43 scripts