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fabMix
View on CRAN: Click
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Download and install fabMix package within the R console
Install from CRAN:
install.packages("fabMix")
Install from Github:
library("remotes")
install_github("cran/fabMix")
Install by package version:
library("remotes")
install_version("fabMix", "5.1")
Attach the package and use:
library("fabMix")
Maintained by
Panagiotis Papastamoulis
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-10-30
Latest Update: 2024-02-12
Description:
Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) (2018) ). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) ): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) ). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) , Papastamoulis (2016) ).
How to cite:
Panagiotis Papastamoulis (2018). fabMix: Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components. R package version 5.1, https://cran.r-project.org/web/packages/fabMix
Previous versions and publish date:
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Functions, R codes and Examples using
the fabMix R package
Some associated functions: CorMat_mcmc_summary . CovMat_mcmc_summary . complete.log.likelihood . complete.log.likelihood_Sj . complete.log.likelihood_q0 . complete.log.likelihood_q0_sameSigma . compute_A_B_G_D_and_simulate_mu_Lambda . compute_A_B_G_D_and_simulate_mu_Lambda_CCU . compute_A_B_G_D_and_simulate_mu_Lambda_CUU . compute_A_B_G_D_and_simulate_mu_Lambda_Sj . compute_A_B_G_D_and_simulate_mu_Lambda_q0 . compute_A_B_G_D_and_simulate_mu_Lambda_q0_sameSigma . compute_sufficient_statistics . compute_sufficient_statistics_given_mu . compute_sufficient_statistics_q0 . dealWithLabelSwitching . fabMix-package . fabMix . fabMix_CxC . fabMix_CxU . fabMix_UxC . fabMix_UxU . fabMix_missing_values . fabMix_parallelModels . getStuffForDIC . log_dirichlet_pdf . myDirichlet . observed.log.likelihood0 . observed.log.likelihood0_Sj . observed.log.likelihood0_Sj_q0 . observed.log.likelihood0_q0_sameSigma . overfittingMFA . overfittingMFA_CCC . overfittingMFA_CCU . overfittingMFA_CUC . overfittingMFA_CUU . overfittingMFA_Sj . overfittingMFA_Sj_missing_values . overfittingMFA_UCC . overfittingMFA_UUC . overfittingMFA_missing_values . overfitting_q0 . overfitting_q0_sameSigma . plot.fabMixject . print.fabMixject . readLambdaValues . simData . simData2 . summary.fabMixject . update_OmegaINV . update_OmegaINV_Cxx . update_SigmaINV_faster . update_SigmaINV_faster_Sj . update_SigmaINV_faster_q0 . update_SigmaINV_faster_q0_sameSigma . update_SigmaINV_xCC . update_SigmaINV_xUC . update_all_y . update_all_y_Sj . update_z2 . update_z2_Sj . update_z4 . update_z4_Sj . update_z_b . update_z_b_Sj . update_z_q0 . update_z_q0_sameSigma . waveDataset1500 .
Some associated R codes: RcppExports.R . fabMix.R . Full fabMix package functions and examples
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