Luca Scrucca
- Molecular Biology
- Artificial Intelligence top 2%
- Ecology top 5%
- Oncology top 10%
- Genetics top 10%
- Co-authors
- Adrian E. RafteryThomas Brendan MurphyMichael FopAnnalisa SantucciFranco AversaChris FraleyPhilip D. ParkerBaljinder K. Sahdra
- Topics
- Bayesian Methods and Mixture Models (18 papers)Advanced Clustering Algorithms Research (8 papers)Gene expression and cancer classification (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaFrontiers in PsychologyJournal of Statistical Software
- Partner nations
- ItalyUnited StatesIreland
In The Last Decade
Luca Scrucca
48 papers receiving 4.1k citations
Hit Papers
Peers
Comparison fields: 5 of 218
- Molecular Biology 584
- Artificial Intelligence 522
- Ecology 320
- Oncology 301
- Genetics 289
Countries citing papers authored by Luca Scrucca
This map shows the geographic impact of Luca Scrucca's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Luca Scrucca with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luca Scrucca more than expected).
Fields of papers citing papers by Luca Scrucca
This network shows the impact of papers produced by Luca Scrucca. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Luca Scrucca. The network helps show where Luca Scrucca may publish in the future.
Co-authorship network of co-authors of Luca Scrucca
This figure shows the co-authorship network connecting the top 25 collaborators of Luca Scrucca. A scholar is included among the top collaborators of Luca Scrucca based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Luca Scrucca. Luca Scrucca is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 8 | |
| 7 | 3 | |
| 8 | 4 | |
| 9 | 10 | |
| 10 | 31 | |
| 11 | 23 | |
| 12 | mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Modelsbreakdown → | 1794 |
| 13 | 54 | |
| 14 | 42 | |
| 15 | GA: A Package for Genetic Algorithms in R | 47 |
| 16 | 3 | |
| 17 | 353 | |
| 18 | Competing risk analysis using R: an easy guide for cliniciansbreakdown → | 535 |
| 19 | Clustering multivariate spatial data based on local measures of spatial autocorrelation | 24 |
| 20 | 9 |
About Luca Scrucca
Luca Scrucca is a scholar working on Statistics and Probability, Tourism, Leisure and Hospitality Management and Artificial Intelligence, having authored 54 papers that have together received 4.2k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (18 papers), Advanced Clustering Algorithms Research (8 papers) and Gene expression and cancer classification (6 papers). The work is most often cited by research in Statistics and Probability (267 citations), Hematology (278 citations) and Ecological Modeling (78 citations). Luca Scrucca has collaborated with scholars based in Italy, United States and Ireland. Frequent co-authors include Adrian E. Raftery, Thomas Brendan Murphy, Michael Fop, Annalisa Santucci, Franco Aversa, Chris Fraley, Philip D. Parker, Baljinder K. Sahdra, Joseph Ciarrochi and Isobel Claire Gormley. Their work appears in journals such as SHILAP Revista de lepidopterología, Frontiers in Psychology and Journal of Statistical Software.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.