Output

Selected publications are given below. Click here to see all my publications.

Monographs
McNicholas, P.D. and Tait, P.A. (2019), Data Science with Julia. Boca Raton: Chapman & Hall/CRC Press. [webpage]

McNicholas, P.D. (2016), Mixture Model-Based Classification. Boca Raton: Chapman & Hall/CRC Press. [webpage]

Forthcoming & Recently Published Contributions (Last 10)
Neal, M.R. and McNicholas, P.D. ‘Parsimonious hidden Markov models for multivariate longitudinal data’, Journal of Classification. To appear. [doi]

Pocuca, N., Gallaugher, M.P.B. and McNicholas, P.D. (2026), ‘Modelling Shanghai soil properties with finite mixtures of SU Johnson distributions’, Computational Statistics. 41, 69. [doi]

Cui, X., Murphy, O.A. and McNicholas, P.D. (2026), ‘Copula-based mixtures of regression models for multivariate response data’, Computational Statistics and Data Analysis 218, 108340. [doi]

Beauchamp, M., Kirkwood, R., Cooper, C., McIlroy, W.E., Van Ooteghem, K., Beyer, K.B., Richardson, J., Kuspinar, A.,  McNicholas, P.D. et al. (2025), ‘Cohort profile: baseline characteristics and design of the McMaster Monitoring My Mobility (MacM3) study – a prospective digital mobility cohort of community-dwelling older Canadians from Southern Ontario’, BMJ Open 15(10), e105223. [doi]

Payne A., Silva A., Rothstein S.J., McNicholas P.D. and Subedi S. (2025), ‘Finite mixtures of multivariate Poisson-log normal factor analyzers for clustering count data’, Statistics and Computing 35, 189. [doi]

Zhang, X., Murphy, O.A. and McNicholas, P.D. (2025), ‘Unbalanced multivariate longitudinal data clustering with a copula kernel mixture model’, Statistics and Computing 35, 126. [doi]

Alamer, E.M.S.,  Gallaugher, M.P.B. and McNicholas, P.D. (2025), ‘A mixture model for skewed mixed-type data’, Statistics and Probability Letters 226, 110507. [doi]

Sochaniwsky, A.A., Gallaugher, M.P.B., Tang, Y. and McNicholas, P.D. (2025), ‘Flexible clustering with a sparse mixture of generalized hyperbolic distributions’, Journal of Classification 42(1), 113-133. [doi]

Zhang, X., Murphy, O.A. and McNicholas, P.D. (2025), ‘Balanced longitudinal data clustering with a copula kernel mixture model’, Canadian Journal of Statistics 53(1), e11838. [doi]

Neal, M.R., Sochaniwsky, A.A., and McNicholas, P.D. (2024), ‘Hidden Markov models for multivariate panel data’, Statistics and Computing 34, 182 . [doi]

Software: Recently Published or Updated
Andrews, J.L., Neal, M.R., and McNicholas, P.D. (2025). vscc: Variable selection for clustering and classification. R package version 0.8.

Clark, K.M. and McNicholas, P.D. (2025), oclust: Gaussian model-based clustering with outliers. R package version 1.0.0.

Zaccaria, G., Cavicchia, C., Balzotti, L., Sochaniwsky, A.A. and McNicholas, P.D. (2025). PUGMM: Parsimonious ultrametric Gaussian mixture models. R package version 0.1.2.

Pocuca, N., Browne, R.P., Sochaniwsky, A.A. and McNicholas, P.D. (2025). mixture: Mixture models for clustering and classification. R package version 2.1.2.

McNicholas, P.D., ElSherbiny, A., Jampani, K.R., McDaid, A.F., Murphy, T.B. and Banks, L. (2025). pgmm: Parsimonious Gaussian mixture models. R package version 1.2.8.