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]

Op-Ed
McNicholas, P.D. ‘Talking about my disorder helped my son — and me’, Toronto Star, A19, March 25, 2023.

Forthcoming & Recently Published Contributions (Last 10)
Cui, X., Murphy, O.A. and McNicholas, P.D., ‘Copula-based mixtures of regression models for multivariate response data’, Computational Statistics and Data Analysis. To appear.

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. and McNicholas, P.D. (2024). ‘Variable selection for clustering three-way data’ in J. Ansari et al. (eds.), Combining, Modelling and Analyzing Imprecision, Randomness and Dependence, Advances in Intelligent Systems and Computing, vol. 1458, Springer Nature Switzerland, pp. 317–324. [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]

Gabour, M.C., You, T., Fleming, R., McNicholas, P.D. and Gona, P.N. (2024), ‘The association of physical activity duration and intensity on emotional intelligence in 10–13 year-old children’, Sports Medicine and Health Science 6(4), 231-237. [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.