Luca Scrucca

Luca Scrucca

Associate Professor of Statistics

Università degli Studi di Perugia

About me

I’m an applied statistician. In a broader sense, I’m also a data scientist because I do data analysis, and I do research on the methodology and computational aspects of data analysis. I’m an enthusiastic R user and package developer.

I’m also serving as Associate Editor for Journal of Statistical Software and Statistics and Computing.

Interests
  • Mixture models
  • Model-based clustering and classification
  • Statistical learning
  • Dimension reduction methods and regression graphics
  • Genetic and evolutionary algorithms
Education

  • PhD in Statistics, 2000

    Università degli Studi di Perugia

  • MSc Statistics, 2000

    University of Minnesota

Research

Academic publications

Book

Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R, Chapman & Hall/CRC, ISBN: 978-1032234953.
Companion book website

Recent publications


Luca Scrucca (2023). Entropy-based anomaly detection for Gaussian mixture modeling. Algorithms, 16:4, 195.

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Stéphane Robin, Luca Scrucca (2023). Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582.

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Luca Scrucca (2022). A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy. Statistical Methods & Applications, 31:4, 881–900.

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Luca Scrucca (2022). Modal clustering on PPGMMGA projection subspace. Australian & New Zealand Journal of Statistics, 64:2, 158–170.

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Alessandro Casa, Luca Scrucca , Giovanna Menardi (2021). Better than the best? Answers via model ensemble in density-based clustering. Advances in Data Analysis and Classification, 15, 599–623.

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Luca Scrucca (2021). A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314.

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Luca Scrucca , Alessio Serafini (2019). Projection pursuit based on Gaussian mixtures and evolutionary algorithms. Journal of Computational and Graphical Statistics, 28:4, 847–860.

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Michael Fop, T. Brendan Murphy, Luca Scrucca (2019). Model-based clustering with sparse covariance matrices. Statistics and Computing, 29:4, 791–819.

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Luca Scrucca (2019). A transformation-based approach to Gaussian mixture density estimation for bounded data. Biometrical Journal, 61:4, 873–888.

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Luca Scrucca , Adrian E. Raftery (2018). clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R. Journal of Statistical Software, 84:1, 1–28.

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See all publications

Software

R packages and functions

I’m the author and/or maintainer of several packages and functions written in R, a free software environment for statistical computing and graphics, using RStudio, an integrated development environment (IDE) for R.

   A collection of R packages for statistical modeling using Gaussian mixtures.

   List of R packages and functions.

Teaching

Courses list


Contact