Michael Fop

3.3k total citations · 1 hit paper
20 papers, 2.0k citations indexed

About

Michael Fop is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Polymers and Plastics. According to data from OpenAlex, Michael Fop has authored 20 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 4 papers in Statistical and Nonlinear Physics and 3 papers in Polymers and Plastics. Recurrent topics in Michael Fop's work include Bayesian Methods and Mixture Models (12 papers), Advanced Clustering Algorithms Research (7 papers) and Complex Network Analysis Techniques (4 papers). Michael Fop is often cited by papers focused on Bayesian Methods and Mixture Models (12 papers), Advanced Clustering Algorithms Research (7 papers) and Complex Network Analysis Techniques (4 papers). Michael Fop collaborates with scholars based in Ireland, Italy and France. Michael Fop's co-authors include Thomas Brendan Murphy, Luca Scrucca, Adrian E. Raftery, Keith M. Smart, Enda Whyte, Noel McCaffrey, Siobhán O’Connor, Kieran Moran, Aisling Ní Annaidh and Michel Destrade and has published in prestigious journals such as Scientific Reports, Acta Biomaterialia and Annals of Biomedical Engineering.

In The Last Decade

Michael Fop

17 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models

2016 1.8k citations Luca Scrucca, Michael Fop et al. The R Journal profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Fop Ireland	7	419	336	203	176	138	20	2.0k
Jochen Kruppa Germany	22	310 0.7×	281 0.8×	130 0.6×	189 1.1×	122 0.9×	47	2.0k
Skipper Seabold United States	3	493 1.2×	373 1.1×	154 0.8×	166 0.9×	254 1.8×	5	3.5k
Luca Scrucca Italy	20	584 1.4×	522 1.6×	320 1.6×	289 1.6×	258 1.9×	54	4.2k
Josef Perktold United States	3	493 1.2×	373 1.1×	154 0.8×	166 0.9×	255 1.8×	3	3.5k
David Welch New Zealand	27	570 1.4×	318 0.9×	279 1.4×	431 2.4×	85 0.6×	72	3.2k
Phillip Good Australia	31	291 0.7×	170 0.5×	111 0.5×	173 1.0×	84 0.6×	143	3.6k
Michael Friendly Canada	25	256 0.6×	582 1.7×	216 1.1×	131 0.7×	182 1.3×	87	3.4k
Gerhard Tutz Germany	9	162 0.4×	372 1.1×	216 1.1×	100 0.6×	203 1.5×	16	2.6k
Garrett Grolemund United States	8	152 0.4×	177 0.5×	317 1.6×	86 0.5×	201 1.5×	9	1.6k
Minoo Niknian United States	17	281 0.7×	129 0.4×	135 0.7×	162 0.9×	73 0.5×	22	2.1k

Countries citing papers authored by Michael Fop

Since Specialization

Citations

This map shows the geographic impact of Michael Fop'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 Michael Fop with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Fop more than expected).

Fields of papers citing papers by Michael Fop

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Fop. 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 Michael Fop. The network helps show where Michael Fop may publish in the future.

Co-authorship network of co-authors of Michael Fop

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Fop. A scholar is included among the top collaborators of Michael Fop 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 Michael Fop. Michael Fop is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

O’Hagan, Adrian, et al.. (2025). A Dirichlet stochastic block model for composition-weighted networks. Computational Statistics & Data Analysis. 211. 108204–108204. 1 indexed citations

Gowen, Aoife, et al.. (2025). A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images. Advances in Data Analysis and Classification. 19(2). 323–359.

Tepole, Adrián Buganza, et al.. (2024). A machine learning approach to predict in vivo skin growth. Scientific Reports. 14(1). 17456–17456. 5 indexed citations

Destrade, Michel, et al.. (2024). A Gaussian process approach for rapid evaluation of skin tension. Acta Biomaterialia. 182. 54–66. 4 indexed citations

Fop, Michael, et al.. (2024). Sparse Model-Based Clustering of Three-Way Data via Lasso-Type Penalties. Journal of Computational and Graphical Statistics. 34(3). 1030–1050.

Gormley, Isobel Claire, et al.. (2024). Variational inference for the latent shrinkage position model. Stat. 13(2).

Gormley, Isobel Claire, et al.. (2023). A Latent Shrinkage Position Model for Binary and Count Network Data. Bayesian Analysis. 20(2). 4 indexed citations

Price, Susan, et al.. (2023). Analysis of In Vivo Skin Anisotropy Using Elastic Wave Measurements and Bayesian Modelling. Annals of Biomedical Engineering. 51(8). 1781–1794. 7 indexed citations

Alfò, Marco, et al.. (2023). Model-based clustering for multidimensional social networks. Journal of the Royal Statistical Society Series A (Statistics in Society). 186(3). 481–507. 3 indexed citations

10.

Fop, Michael, et al.. (2022). Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering. Journal of Classification. 39(3). 648–674. 3 indexed citations

11.

Fop, Michael, et al.. (2021). Unobserved classes and extra variables in high-dimensional discriminant\n analysis. arXiv (Cornell University). 1 indexed citations

12.

O’Connor, Siobhán, Noel McCaffrey, Enda Whyte, et al.. (2020). Can the Y balance test identify those at risk of contact or non-contact lower extremity injury in adolescent and collegiate Gaelic games?. Journal of science and medicine in sport. 23(10). 943–948. 10 indexed citations

13.

Fop, Michael, et al.. (2020). A stochastic block model for interaction lengths. Advances in Data Analysis and Classification. 14(2). 485–512. 5 indexed citations

14.

Fop, Michael. (2018). Variable Selection for Latent Class Analysis [R package LCAvarsel version 1.1]. 2 indexed citations

15.

Fop, Michael, Thomas Brendan Murphy, & Luca Scrucca. (2018). Model-based clustering with sparse covariance matrices. Statistics and Computing. 29(4). 791–819. 20 indexed citations

16.

O’Connor, Siobhán, Noel McCaffrey, Enda Whyte, et al.. (2018). Is Poor Hamstring Flexibility a Risk Factor for Hamstring Injury in Gaelic Games?. Journal of Sport Rehabilitation. 28(7). 677–681. 7 indexed citations

17.

Fop, Michael & Thomas Brendan Murphy. (2018). Variable selection methods for model-based clustering. arXiv (Cornell University). 12(none). 62 indexed citations

18.

Scrucca, Luca, Michael Fop, Thomas Brendan Murphy, & Adrian E. Raftery. (2017). The mclust R package for clustering, classification and density estimation using Gaussian finite mixture models. 175–175. 2 indexed citations

19.

Fop, Michael, Keith M. Smart, & Thomas Brendan Murphy. (2017). Variable selection for latent class analysis with application to low back pain diagnosis. The Annals of Applied Statistics. 11(4). 33 indexed citations

20.

Scrucca, Luca, Michael Fop, Thomas Brendan Murphy, & Adrian E. Raftery. (2016). mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. The R Journal. 8(1). 289–289. 1794 indexed citations breakdown →

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.

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