| mclust 5: clustering, classification and density estimation using Gaussian finite mixture models L Scrucca, M Fop, TB Murphy, AE Raftery The R journal 8 (1), 289, 2016 | 2984 | 2016 |
| Variable selection methods for model-based clustering M Fop, TB Murphy | 141 | 2018 |
| mclust: Gaussian mixture modelling for model-based clustering, classification, and density estimation C Fraley, AE Raftery, L Scrucca, TB Murphy, M Fop R package version 5, 2021 | 71* | 2021 |
| Variable selection for latent class analysis with application to low back pain diagnosis M Fop, KM Smart, TB Murphy The Annals of Applied Statistics, 2080-2110, 2017 | 67 | 2017 |
| Package ‘mclust’ C Fraley, AE Raftery, L Scrucca, TB Murphy, M Fop, ML Scrucca | 41 | 2012 |
| Model-based clustering with sparse covariance matrices M Fop, TB Murphy, L Scrucca Statistics and Computing 29 (4), 791-819, 2019 | 32 | 2019 |
| Can the Y balance test identify those at risk of contact or non-contact lower extremity injury in adolescent and collegiate Gaelic games? S O’Connor, N McCaffrey, EF Whyte, M Fop, B Murphy, K Moran Journal of science and medicine in sport 23 (10), 943-948, 2020 | 21 | 2020 |
| Is poor hamstring flexibility a risk factor for hamstring injury in Gaelic games? S O’Connor, N McCaffrey, EF Whyte, M Fop, B Murphy, KA Moran Journal of Sport Rehabilitation 28 (7), 677-681, 2019 | 20 | 2019 |
| mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 2016; 8 (1): 289–317. doi: 10.32614 L Scrucca, M Fop, TB Murphy, AE Raftery RJ-2016-021.[Europe PMC free article][Abstract][CrossRef][Academic Search], 0 | 20 | |
| Mclust: Gaussian mixture modelling for model-based clustering, classification, and density estimation, R package Version 5.3 C Fraley, AE Raftery, L Scrucca, TB Murphy, M Fop R-project. org/package= mclust, 2017 | 18 | 2017 |
| Model-based clustering for multidimensional social networks S D’Angelo, M Alfò, M Fop Journal of the Royal Statistical Society Series A: Statistics in Society 186 …, 2023 | 15* | 2023 |
| A stochastic block model for interaction lengths R Rastelli, M Fop Advances in Data Analysis and Classification 14 (2), 485-512, 2020 | 13 | 2020 |
| A latent shrinkage position model for binary and count network data XY Gwee, IC Gormley, M Fop Bayesian Analysis 1 (1), 1-29, 2023 | 7 | 2023 |
| Group-wise shrinkage estimation in penalized model-based clustering A Casa, A Cappozzo, M Fop Journal of Classification 39 (3), 648-674, 2022 | 7 | 2022 |
| R package ‘mclust’: Gaussian mixture modelling for model-based clustering, classification, and density estimation C Fraley, AE Raftery, L Scrucca, TB Murphy, M Fop The Comprehensive R Archive Network, 2016 | 6 | 2016 |
| mclust: Normal Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation, 2016 C Fraley, AE Raftery, L Scrucca, TB Murphy, M Fop URL http://CRAN. R-project. org/package= mclust. R package version 5, 0 | 6 | |
| Analysis of in vivo skin anisotropy using elastic wave measurements and Bayesian modelling M Nagle, S Price, A Trotta, M Destrade, M Fop, A Ní Annaidh Annals of Biomedical Engineering 51 (8), 1781-1794, 2023 | 5 | 2023 |
| Unobserved classes and extra variables in high-dimensional discriminant analysis M Fop, PA Mattei, C Bouveyron, TB Murphy Advances in Data Analysis and Classification 16 (1), 55-92, 2022 | 3 | 2022 |
| Sparse model-based clustering of three-way data via lasso-type penalties A Cappozzo, A Casa, M Fop Journal of Computational and Graphical Statistics, 1-21, 2024 | 1 | 2024 |
| A Dirichlet stochastic block model for composition-weighted networks I Promskaia, A O'Hagan, M Fop arXiv preprint arXiv:2408.00651, 2024 | 1 | 2024 |
Brendan MurphyUniversity College DublinAdresse e-mail validée de ucd.ie