## mclust: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation |

- 'mclust' is available on CRAN.
- Author: Chris Fraley, Adrian Raftery and Luca Scrucca
- Wepage: http://www.stat.washington.edu/mclust/
- Package vignette: Chris Fraley, Adrian E. Raftery, Thomas Brendan Murphy, and Luca Scrucca (2012) MCLUST Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation, Technical Report no. 597, Department of Statistics, University of Washington.

## clustvarsel: Variable Selection for Model-Based Clustering |

- 'clustvarsel' is available on CRAN.
- Author: Nema Dean, Adrian E. Raftery, and Luca Scrucca
- Scrucca L. and Raftery A. E. (2015) clustvarsel: A Package Implementing Variable Selection for
Model-based Clustering in R. Submitted to
*Journal of Statistical Software*. Pre-print available on arXiv.

## GA: Genetic Algorithms |

- 'GA' is available on CRAN.
- Luca Scrucca (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. http://www.jstatsoft.org/v53/i04/

## qcc: Quality Control Charts |

A short description of the package appeared in the newsletter

The 'qcc' package is included in the 10 R packages I wish I knew about earlier blog post.

A nice overview is also given in Statistical Quality Control in R.

## msir: Model-based Sliced Inverse Regression |

References:

- Scrucca L. (2011) Model-based SIR for dimension reduction.
Computational Statistics & Data Analysis, Vol. 55, Issue 11,
pp. 3010-3026.

Supplemental material.

## GAabbreviate: Abbreviating Items Measures using Genetic Algorithms |

- 'GAabbreviate' is available on CRAN.
- Sahdra B. K., Ciarrochi J., Parker P. and Scrucca L. (2016). Using genetic algorithms in a large nationally
representative American sample to abbreviate the Multidimensional Experiential Avoidance Questionnaire.
*Frontiers in Psychology*, Vol. 7(189), pp. 1--14. http://www.frontiersin.org/quantitative_psychology_and_measurement/10.3389/fpsyg.2016.00189/abstract

## Regularized Sliced Inverse Regression |

- Scrucca L. (2006) Regularized sliced inverse regression with applications in classification (2006). In "Data Analysis, Classification and the Forward Search", editors Zani S., Cerioli A., Riani M., Vichi M., Berlin, Springer-Verlag, pp. 59-66.

- Scrucca L. (2007) Class prediction and gene selection for DNA microarrays using sliced inverse regression (2007). Computational Statistics & Data Analysis, Vol. 52, pp. 438-451.

## Competing Risks Analysis |

Scrucca L., Santucci A., Aversa F. (2007) Competing risks analysis using R: an easy guide for clinicians.

Functions (based on the cmprsk R package) and dataset for regression modeling of competing risk. Reference:

Scrucca L., Santucci A., Aversa F. (2009) Regression modeling of competing risk using R: an in depth guide for clinicians. To appear in

## dispmod: Dispersion Models |

## forward: Forward search |

Last modified: 8/3/2013