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]

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)
Neal, M. 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]

Zhang, X., Murphy, O.A. and McNicholas, P.D., ‘Balanced longitudinal data clustering with a copula kernel mixture model’, Canadian Journal of Statistics. To appear.

Sochaniwsky, A.A., Gallaugher, M.P.B., Tang, Y. and McNicholas, P.D., ‘Flexible clustering with a sparse mixture of generalized hyperbolic distributions’, Journal of Classification. To appear. [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]

Gallaugher, M.P.B. and McNicholas, P.D. (2024), ‘Clustering and semi-supervised classification for clickstream data via mixture models’, Canadian Journal of Statistics. 52(3), 678-695. [doi]

Clark, K.M. and McNicholas, P.D. (2024), ‘Finding outliers in Gaussian model-based clustering', Journal of Classification 41(2), 313-337. [doi]

Pocuca, N., Gallaugher, M.P.B., Clark, K.M. and McNicholas, P.D. (2023), ‘Visual assessment of matrix-variate normality’, Australian and New Zealand Journal of Statistics 65(2), 152-165. [doi]

Gallaugher, M.P.B., Biernacki, C. and McNicholas, P.D. (2023), ‘Parameter-wise co-clustering for high-dimensional data’, Computational Statistics 38, 1597-1619. [doi]

Silva, A., Qin, X., Rothstein, S.J., McNicholas, P.D. and Subedi, S. (2023), ‘Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data’, Bioinformatics 39(5), btad167. [doi]

Software: Recently Published or Updated
Neal, M.R., Sochaniwsky, A.A., and McNicholas, P.D. (2024). CDGHMM: Hidden Markov models for multivariate panel data. R package version 0.1.0.

Pocuca, N., Browne, R.P., and McNicholas, P.D. (2024). mixture: Mixture models for clustering and classification. R package version 2.1.1.

McNicholas, P.D., Jampani, K.R., Subedi, S. (2023). longclust: Clustering longitudinal data. R package version 1.5.

McNicholas, P.D., ElSherbiny, A., Jampani, K.R., McDaid, A.F., Murphy, T.B. and Banks, L. (2023). pgmm: Parsimonious Gaussian mixture models. R package version 1.2.7.

Andrews, J.L., Neal, M.R., and McNicholas, P.D. (2023). vscc: Variable selection for clustering and classification. R package version 0.7.