Luca Scrucca

Prof. Dr. Luca Scrucca

Department of Economics, University of Perugia, Perugia, Umbria, Italy
orcid

Research Focus & Metrics

Academic Career Length

28Years (1999-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 January 2016 in The R Journal
Verified
Journal Article
Open access
LSLuca Scrucca
Luca Scrucca
MFMichael Fop
Michael Fop
BTBrendan Murphy T.
Brendan Murphy T.
EAE. Raftery Adrian
E. Raftery Adrian


1 January 2013 in Journal of Statistical Software
Verified
Journal Article
Open access
LSLuca Scrucca
Luca Scrucca
GA: A Package for Genetic Algorithms in R

11 June 2007 in Bone Marrow Transplantation
Verified
Journal Article
Closed access
LSLuca Scrucca
Luca Scrucca
ASA Santucci
A Santucci
FAF Aversa
F Aversa

11 January 2010 in Bone Marrow Transplantation
Verified
Journal Article
Closed access
LSLuca Scrucca
Luca Scrucca
ASA Santucci
A Santucci
FAF Aversa
F Aversa

21 March 2023
Verified
Book
Closed access
PDF file
LSLuca Scrucca
Luca Scrucca
CFChris Fraley
Chris Fraley
TMThomas Brendan Murphy
Thomas Brendan Murphy
RERaftery Adrian E.
Raftery Adrian E.
Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.