Publications

Preprints

  • Sweeney J., Haslett J., Bandyopadhyay D., Fop M., Parnell A. C. (2025)
    Zero and N-inflated overdispersed binomial models for sum-constrained Poisson count processes.
    Preprint. arXiv

  • Clarke CJ, Fop M. (2025).
    A latent position co-clustering model for multiplex networks.
    Preprint. arXiv

  • Fritz C., Rastelli R., Fop M., Caimo A. (2025).
    Scalable durational event models: Application to physical and digital interactions.
    Preprint. arXiv

  • Promskaia I., O’Hagan A., Fop M. (2024).
    Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networks.
    Preprint. arXiv

  • Pacheco Menezes T., Murphy T. B., Fop M. (2024)
    Hausdorff distance-based record linkage for improved matching of households and individuals in different databases.
    Preprint. arXiv

Articles

  • Gwee X. Y., Gormley I. C., Fop M. (2025)
    Model-based clustering for network data via a latent shrinkage position cluster model.
    Network Science, to appear. arXiv | Code

  • Promskaia I., O’Hagan A., Fop M. (2025)
    A Dirichlet stochastic block model for composition-weighted networks.
    Computational Statistics and Data Analysis, 211, 108204. Link | arXiv | Code

  • Babu G., Gowen A., Fop M., Gormley I. C. (2025)
    A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images.
    Advances in Data Analysis and Classification, 19, 323-359. Link | arXiv | Code

  • Cappozzo A., Casa A., Fop M. (2025)
    Sparse model-based clustering of three-way data via lasso-type penalties.
    Journal of Computational and Graphical Statistics, 34(3), 1030-1050. Link | arXiv | Code

  • Gwee X. Y., Gormley I. C., Fop M. (2025)
    A latent shrinkage position model for binary and count network data.
    Bayesian Analysis, 20(2), 405-433. Link | arXiv | Code

  • Nagle M., Broderick H. C., Buganza Tepole A., Fop M., Ní Annaidh A. (2024)
    A machine learning approach to predict in vivo skin growth.
    Scientific Reports, 14, 17456. Link

  • Nagle M., Broderick H. C., Vedel C., Destrade M., Fop M., Ní Annaidh A. (2024)
    A Gaussian process approach for rapid evaluation of skin tension.
    Acta Biomaterialia, 182 54-66. Link

  • Gwee X. Y., Gormley I. C., Fop M. (2024)
    Variational inference for the latent shrinkage position model.
    Stat, 13(2), e685. Link | arXiv | Code

  • Nagle M., Price S., Trotta A., Destrade M., Fop M., Ní Annaidh A. (2023)
    Analysis of in vivo skin anisotropy using elastic wave measurements and Bayesian modelling.
    Annals of Biomedical Engineering, 51:1781-1794. Link

  • D’Angelo S., Alfò M., Fop M. (2023)
    Model-based clustering for multidimensional social networks.
    Journal of the Royal Statistical Society Series A: Statistics in Society, 186(3), 481-507. Link | arXiv

  • Casa A., Cappozzo A., Fop M. (2022)
    Group-wise shrinkage estimation in penalized model-based clustering.
    Journal of Classification, 39:648-674. Link | arXiv | Code

  • Fop M., Mattei P-A., Bouveyron C., Murphy T.B. (2022)
    Unobserved classes and extra variables in high-dimensional discriminant analysis.
    Advances in Data Analysis and Classification, 16, 55-92. Link | arXiv | R package

  • Rastelli R., Fop M. (2020)
    A stochastic blockmodel for interaction lengths.
    Advances in Data Analysis and Classification, 14, 485-512. Link | arXiv | R package

  • O’Connor S., McCaffrey N., Whyte E.F., Fop M., Murphy T.B., Moran K.A. (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. Link

  • Fop M., Murphy T.B., Scrucca L. (2019)
    Model-based clustering with sparse covariance matrices.
    Statistics and Computing, 29(4), 791–819. Link | arXiv | R package

  • O’Connor S., McCaffrey N., Whyte E.F., Fop M., Murphy T.B., Moran K.A. (2018)
    Is poor hamstring flexibility a risk factor for hamstring injury in Gaelic games?
    Journal of Sport Rehabilitation, 28(7), 677-681. Link

  • Fop M., Murphy T.B. (2018)
    Variable selection methods for model-based clustering.
    Statistics Surveys, 12, 18-65. Link | arXiv | R code

  • Fop M., Smart K., Murphy T.B. (2017)
    Variable selection for latent class analysis with application to low back pain diagnosis.
    Annals of Applied Statistics, 11(4), 2085–2115. Link | arXiv | R package

  • Scrucca L., Fop M., Murphy, T.B. Raftery A.E. (2016)
    mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models.
    The R Journal, 8(1), 289-317. Link | R package

Discussion papers

  • Casa, A., Fop, M. & D’Angelo, S. (2024)
    Contributed discussion to “Sparse Bayesian factor analysis when the number of factors is unknown” by Frühwirth-Schnatter, S., Hosszejni, D. & Freitas Lopes, H.
    Bayesian Analysis (in press). Link

  • Wyse J., Ng J., White A., Fop M. (2024)
    Contributed discussion to “Root and community inference on the latent growth process of a network”, by Crane H., Hu M.
    Journal of the Royal Statistical Society Series B: Statistical Methodology, 86(4), 884–885. | Link

  • Casa A., Fop M., Murphy T.B. (2021)
    Contributed discussion to “Centered partition processes: informative priors for clustering” by Paganin, S., Herring, A.H., Olshan, A.F., Dunson, D.B.
    Bayesian Analysis, 16(1), 301-370. Link