Luca Scrucca

Full Professor of Statistics
Department of Statistical Sciences
Alma Mater Studiorum – University of Bologna

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Academic Bio

Hello, 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
  • Data science
  • Statistical machine learning
  • Dimension reduction methods and regression graphics
  • Genetic and evolutionary algorithms

Education

PhD in Statistics
Università degli Studi di Perugia
2000

MSc Statistics
University of Minnesota
2000

Research

Standing on the shoulders of giants.
— Bernard of Chartres

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 Press.

ISBN: 978-1032234953
eBook ISBN: 978-1003277965

Online book

Selected publications

  Scrucca L., Karlis D. (2025) A model-based approach to shot charts estimation in basketball. Computational Statistics, 40(1), 2031—2048. https://doi.org/10.1007/s00180-025-01599-1

  Scrucca L. (2024) Entropy-based volatility analysis of financial log-returns using Gaussian mixture models. Entropy, 26(11), 907. https://doi.org/10.3390/e26110907

  Scrucca L. (2023) Entropy-based anomaly detection for Gaussian mixture modeling. Algorithms, 16(4), 195. https://doi.org/10.3390/a16040195

  Robin S., Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. https://doi.org/10.1016/j.csda.2022.107582

  Scrucca L. (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. https://doi.org/10.1007/s10260-021-00617-y

  Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. https://doi.org/10.1002/sam.11527

  Casa A., Scrucca L., Menardi G. (2021) Better than the best? Answers via model ensemble in density-based clustering. Advances in Data Analysis and Classification, 15, 599—623. https://doi.org/10.1007/s11634-020-00423-6

  Scrucca L., Serafini A. (2019) Projection pursuit based on Gaussian mixtures and evolutionary algorithms. Journal of Computational and Graphical Statistics, 28:4, 847–860. https://doi.org/10.1080/10618600.2019.1598871

  Fop M., Murphy T. B., Scrucca L. (2019) Model-based clustering with sparse covariance matrices. Statistics and Computing, 29:4, 791–819. https://doi.org/10.1007/s11222-018-9838-y

  Scrucca L. (2019) A transformation-based approach to Gaussian mixture density estimation for bounded data. Biometrical Journal, 61:4, 873–888. https://doi.org/10.1002/bimj.201800174

  Scrucca L. and Raftery A. E. (2018) clustvarsel: A Package Implementing Variable Selection for Gaussian Model-based Clustering in R. Journal of Statistical Software, 84:1, 1–28. http://doi.org/10.18637/jss.v084.i01

  Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8:1, 205–233. https://doi.org/10.32614/RJ-2016-021

  Scrucca L. (2013) GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 5:4, 1–37. http://www.jstatsoft.org/v53/i04

  Full list of publications

Software

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

  Statistica
      Laurea in “Statistica, Finanza e Assicurazioni” (CLASFA)

  Analisi delle Serie Storiche per la Finanza e le Assicurazioni (Modulo 1)
      Laurea in “Statistica, Finanza e Assicurazioni” (CLASFA)

  Statistica Avanzata (Modulo 2)
      Laurea Magistrale in “Scienze Statistiche, Finanziarie e Attuariali” (SSFA)

  Statistica
      Dipartimento di Economia (sede di Terni)
      Università degli Studi di Perugia


  UniBO Virtuale

  AlmaEsami

  UniBO EOL


Contact

Prof. Luca Scrucca
    Alma Mater Studiorum – Università di Bologna
    Dipartimento di Scienze Statistiche “Paolo Fortunati”
     Piazzetta Teatini 10, 47921, Rimini RN (Italy)
    https://www.unibo.it/sitoweb/luca.scrucca