EMMIXmfa

We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) doi:10.1002/0471721182.ch8 McLachlan GJ, Peel D (2000) ISBN:1-55860-707-2 McLachlan GJ, Peel D, Bean RW (2003) doi:10.1016/S0167-9473(02)00183-4 McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) doi:10.1016/j.csda.2006.09.015 Baek J, McLachlan GJ, Flack LK (2010) doi:10.1109/TPAMI.2009.149 Baek J, McLachlan GJ (2011) doi:10.1093/bioinformatics/btr112 McLachlan GJ, Baek J, Rathnayake SI (2011) doi:10.1002/9781119995678.ch9.

Last 30 days

This package has been downloaded 175 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.

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Last 365 days

This package has been downloaded 2,820 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was May 07, 2024 with 38 downloads.

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