fabMix

News and Updates

[February 20, 2020] Version 5.0 available on CRAN.

Now the lower triangular expansion on the matrix of loadings is optional:

lowerTriangular: logical value indicating whether a lower triangular parameterization should be imposed on the matrix of factor loadings (if TRUE) or not. Default: TRUE.

[February 10, 2020] Version 5.0 (not yet on CRAN).

Now the lower triangular expansion on the matrix of loadings is optional.

[January 21, 2020] Version 4.6 submitted to CRAN.

In this new version I have removed all dependence on the orphaned doRNG package.

[August 15, 2019] Paper accepted to Statistics and Computing.

Pre-print available on arXiv: Clustering Multivariate Data using Factor Analytic Bayesian Mixtures with an Unknown Number of Components

[June 04, 2019] New pre-print available on arXiv

Clustering Multivariate Data using Factor Analytic Bayesian Mixtures with an Unknown Number of Components

[Jan 08, 2019] fabMix version 4.5 uploaded to CRAN

Bug fix in plot for Lamba_map.

[December 23, 2018] fabMix version 4.5 uploaded

Bug fix in plot for Lamba_map.

[November 28, 2018] fabMix version 4.4 available on CRAN

Improved plot and summary methods. See CRAN page.

[November 21, 2018] fabMix version 4.4 edited

Improved plot and summary methods. This new version will be available to CRAN later this month.

[November 9, 2018] fabMix version 4.4 uploaded

This version fixes a bug to output which caused the main function to crash at certain cases. Not on CRAN yet.

[October 30, 2018] fabMix version 4.3 available on CRAN

See CRAN page

[October 8, 2018] fabMix version 4.3 added (currently under development):

The fabMix function now features a new argument (parallelModels), allowing the user to run different models in parallel. This is combined with the pre-existing option to run heated chains in parallel, thus, parallelization is now implemented in both model-level and chain-level.

[October 2, 2018] fabMix version 4.2 added with Windows compatibility.

[September 2018] Versions 3 and 4 added, which contain a plethora of new models and functionalities.

Version 3.0 introduces new parsimonious models.

Since version 4.0, the package is integrated with C++ code.

Version 4.1 improves the Lambda output: all MCMC values are now exported to a single file (instead of multiple ones, as done in previous versions).

[March 2018] Paper accepted to Computational Statistics & Data Analysis

Please use version 2.0 of the software for the relevant R scripts. But note that

1. Recent versions are recommended.
2. All package versions smaller than 4.2 are compatible only with Linux distributions.

[September 2017] Version_2.0 added with support for missing values 🌔