Publications

Preprints

  • Promskaia I., O’Hagan A., Fop M. (2024)
    A Dirichlet stochastic block model for composition-weighted networks.
    Preprint. arXiv | Code

  • 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

  • Babu G., Gowen A., Fop M., Gormley I. C. (2024)
    A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images.
    Preprint. arXiv

  • Gwee X. Y., Gormley I. C., Fop M. (2023)
    Model-based clustering for network data via a latent shrinkage position cluster model.
    Preprint. arXiv | Code

  • Cappozzo A., Casa A., Fop M. (2023)
    Sparse model-based clustering of three-way data via lasso-type penalties.
    Preprint. arXiv

Articles

  • 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

  • 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, online. Link | arXiv | Code

  • Gwee X. Y., Gormley I. C., Fop M. (2023)
    A latent shrinkage position model for binary and count network data.
    Bayesian Analysis, online. 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

  • 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

  • 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