# Paul McNicholas

# All 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]

## Software

## 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.

## Clark, K.M. and McNicholas, P.D. (2022), oclust: Gaussian model-based clustering with outliers. R package version 0.2.0.

## Athey, T.B.T., McNicholas, P.D. and Phillips, J. (2022). VLF: Frequency matrix approach for assessing very low frequency variants in sequence records. R package version 1.1.

## Tortora, C., Vidales, N., Palumbo, F. and McNicholas, P.D. (2022). FPDclustering: PD-clustering and factor PD-clustering. R package version 1.4.1.

## Punzo, A., Mazza, A. and McNicholas, P.D. (2022). ContaminatedMixt: Model-based clustering and classification with the multivariate contaminated normal distribution. R package version 1.3.7.

## Tortora, C., ElSherbiny A., Browne, R.P., Franczak, B.C., McNicholas, P.D. and Amos, D.D. (2022). MixGHD: Model based clustering, classification and discriminant analysis usingthe mixture of generalized hyperbolic distributions. R package version 2.3.7.

## Browne, R.P., Dang, U.J., Gallaugher, M.P.B. and McNicholas, P.D. (2021), mixSPE: Mixtures of power exponential and skew power exponential distributions for use in model-based clustering and classification. R package version 0.9.1.

## Pocuca, N., Gallaugher, M.P.B. and McNicholas, P.D. (2019), MatrixVariate.jl: A complete statistical framework for analyzing matrix variate data. Julia package version 0.2.0.

## Gallaugher, M.P.B. and McNicholas, P.D. (2019), ClickClustCont: Mixtures of continuous time Markov models. R package version 0.1.7.

## Andrews, J.L., Wickins, J.R., Boers, N.M. and McNicholas, P.D. (2018). teigen: Model-based clustering and classifi cation with the multivariate t-distribution. R package version 2.2.2.

## Franczak, B.C., Browne, R.P. and McNicholas, P.D. (2016). sensory: Simultaneous model-based clustering and imputation via a progressive expectation-maximization algorithm. R package version 1.1.

## Journal Articles

## 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.

## Neal, M.R., Sochaniwsky, A.A., and McNicholas, P.D., ‘Hidden Markov models for multivariate panel data’, Statistics and Computing. To appear. [doi]

## 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].

## 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. 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]

## Dang, U.J., Gallaugher, M.P.B., Browne, R.P., and McNicholas, P.D. (2023), ‘Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions’, Journal of Classification 40(1), 145-167. [doi]

## Phillips, J.D., Athey, T.B.T., McNicholas, P.D. and Hanner, R.H. (2023), ‘VLF: An R package for the analysis of very low frequency variants in DNA sequences’, Biodiversity Data Journal 11: e96480. [doi]

## Gallaugher, M.P.B., Tomarchio, S.D., McNicholas, P.D. and Punzo, A. (2022), ‘Model-based clustering via skewed matrix-variate cluster-weighted models’, Journal of Statistical Computation and Simulation 31(2), 413-421. [doi]

## Gallaugher, M.P.B., Tomarchio, S.D., Punzo, A. and McNicholas, P.D. (2022), 'Mixtures of contaminated matrix variate normal distributions', Journal of Computational and Graphical Statistics 31(2), 413-421. [doi]

## Gallaugher, M.P.B., Tomarchio, S.D., McNicholas, P.D. and Punzo, A. (2022), 'Multivariate cluster weighted models using skewed distributions', Advances in Data Analysis and Classification 16(1), 93-124. [doi]

## Browne, R.P., McNicholas, P.D. and Findlay, C. (2022), ‘A partial EM algorithm for model‐based clustering with highly diverse missing data patterns’, Stat 11(1), e437. [doi]

## Georgiades, S., Tait, P.A., McNicholas, P.D., Duku, E., Zwaigenbaum, L., Smith, I.M., Bennett, T., Elsabbagh, M., Kerns, C.M., Mirenda, P., Ungar, W.J., Vaillancourt, T., Volden, J., Waddell, C., Zaidman-Zait, A., Gentles, S. and Szatmari, P.M. (2022), ‘Trajectories of symptom severity in children with autism: Variability and turning points through the transition to school’, Journal of Autism and Developmental Disorders 52(1),392-401. [doi] (open access)

## Tomarchio, S.D., McNicholas, P.D. and Punzo, A. (2021), ‘Matrix normal cluster-weighted models’, Journal of Classification 38(3), 556–575. [doi]

## Vrkljan, B., Beauchamp, M.K., Gardner, P., Fang, Q., Kuspinar, A., McNicholas, P.D., .Newbold, K.B., Richardson, J., Scott, D., Zargoush, M., andGruppuso, V. (2021) ,‘Re-engaging in aging and mobility research in the COVID-19 era: Early lessons from pivoting a large-scale, interdisciplinary study amidst a pandemic’, Canadian Journal on Aging 40(4), 669-675. [doi]

## Tang, Y., Qazi, M.A., Brown, K.R., Mikolajewicz, N., Moat, J., Singh, S.K. and McNicholas, P.D. (2021), ‘Identification of five important genes to predict glioblastoma subtypes', Neuro-Oncology Advances 3(1), vdab144. [doi]

## McNicholas, S.M., McNicholas, P.D. and Ashlock, D.A. (2021), 'An evolutionary algorithm with crossover and mutation for model-based clustering', Journal of Classification 38(2), 264-279. [doi]

## Tortora, C., Browne, R.P., ElSherbiny, A., Franczak, B.C., McNicholas, P.D. (2021), ‘Model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution: MixGHD R package’, Journal of Statistical Software 98:3. [doi]

## Subedi, S. and McNicholas, P.D. (2021), ‘A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting’, Journal of Classification 38(1), 89–108. [doi]

## Roick, T., Karlis, D. and McNicholas, P.D. (2021), ‘Clustering discrete-valued time series’, Advances in Data Analysis and Classification 15(1), 209-229. [doi]

## Mayhew, A.J., Phillips, S.M., Sohel, N.,Thabane, L., McNicholas, P.D., de Souza, R.J., Parise, G. and Raina, P. (2021), ‘Methodological issues and the impact of age stratification on the proportion of participants with low appendicular lean mass when adjusting for height and fat mass using linear regression: Results from the Canadian Longitudinal Study on Aging’, The Journal of Frailty and Aging 10, 150-155. [doi]

## Mayhew, A.J., Phillips, S.M., Sohel, N.,Thabane, L., McNicholas, P.D., de Souza, R.J., Parise, G. and Raina, P. (2021), ‘Do different ascertainment techniques identify the same individuals as sarcopenic in the Canadian Longitudinal Study on Aging?’, Journal of the American Geriatrics Society 69(1), 164-172. [doi]

## Mayhew, A.J., Phillips, S.M., Sohel, N.,Thabane, L., McNicholas, P.D., de Souza, R.J., Parise, G. and Raina, P. (2020), ‘The impact of different diagnostic criteria on the association of sarcopenia with injurious falls in the CLSA’, Journal of Cachexia, Sarcopenia and Muscle 11(6), 1603-1613. [doi] (open access)

## Paton, F. and McNicholas, P.D. (2020), ‘Detecting British Columbia coastal rainfall patterns by clustering Gaussian processes’, Environmetrics 31(8), e2631. [doi]

## Murray, P.M., Browne, R.P. and McNicholas, P.D. (2020), 'Mixtures of hidden truncation hyperbolic factor analyzers', Journal of Classification 37(2), 366-379. [doi]

## Gallaugher, M.P.B. and McNicholas, P.D. (2020), ‘Mixtures of skewed matrix variate bilinear factor analyzers', Advances in Data Analysis and Classification 14(2), 415-434. [doi]

## Pocuca, N., Jevtic, P., McNicholas, P.D. and Miljkovic, T. (2020), ‘Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models’, Insurance: Mathematics and Economics 94, 79-93. [doi]

## Wei , Y., Tang, Y. and McNicholas, P.D. (2020), 'Flexible high-dimensional unsupervised learning with missing data’, IEEE Transactions on Pattern Analysis and Machine Intelligence 42(3), 610-621. [doi]

## Tortora, C., McNicholas, P.D. and Palumbo, F. (2020), ‘A probabilistic distance clustering algorithm using Gaussian and Student-t multivariate density distributions’, SN Computer Science 1(2): 65. [doi]

## Punzo, A., Blostein, M. and McNicholas, P.D. (2020), ‘High-dimensional unsupervised classification via parsimonious contaminated mixtures', Pattern Recognition 98:107031. [doi]

## Gallaugher, M.P.B. and McNicholas, P.D. (2019), 'On fractionally-supervised classification: Weight selection and extension to the multivariate t-distribution', Journal of Classification 36(2), 232-265. [doi]

## Turco, C.V., Pesevski, A., McNicholas, P.D., Beaulieu, L.-D. and Nelson, A.J. (2019), 'Reliability of transcranial magnetic stimulation measures of afferent inhibition', Brain Research 1723:146394. [doi]

## Silva, A., Rothstein, S.J., McNicholas, P.D. and Subedi, S. (2019), 'A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data', BMC Bioinformatics 20:394. [doi]

## Tortora, C., Franczak, B.C., Browne, R.P. and McNicholas, P.D. (2019), 'A mixture of coalesced generalized hyperbolic distributions', Journal of Classification 36(1), 26-57. [doi]

## Murray, P.M., Browne, R.P. and McNicholas, P.D. (2019), Note of Clarification on 'Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering, by Murray, Browne, and McNicholas, J. Multivariate Analysis 161 (2017) 141-156.', Journal of Multivariate Analysis 171, 475-476. [doi]

## Morris, K., Punzo, A., McNicholas, P.D. and Browne, R.P. (2019), 'Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions', Computational Statistics and Data Analysis 132, 145-166. [doi]

## Mayhew, A.J., Amog, K., Phillips, S., Parise, G., McNicholas, P.D., de Souza, R.J., Thabane, L. and Raina P. (2019), 'The prevalence of sarcopenia in community dwelling older adults, an exploration of differences between studies and within definitions: A systematic review and meta-analyses', Age and Aging 48(1), 48-56. [doi]

## Gallaugher, M.P.B. and McNicholas, P.D. (2019), 'Three skewed matrix variate distributions', Statistics and Probability Letters 145, 103-109. [doi]

## Wei, Y., Tang, Y. and McNicholas, P.D. (2019), 'Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data', Computational Statistics and Data Analysis 130, 18-41. [doi]

## Shaikh, M.R., McNicholas, P.D., Antonie, L.M. and Murphy, T.B. (2018), 'Standardizing interestingness measures for association rules', Statistical Analysis and Data Mining 11(6), 282-295. [doi]

## Jones, A., Costa, A.P., Pesevski, A. and McNicholas, P.D. (2018), 'Predicting hospital and emergency department utilization among community-dwelling older adults: statistical and machine learning approaches', PLOS ONE 13(11):e0206662. [doi]

## Morton, R.W., Sato, K., Gallaugher, M.P.B., Oikawa, S.Y., McNicholas, P.D., Fujita, S. and Phillips, S.M. (2018), 'Muscle androgen receptor content but not systemic hormones is associated with resistance training-induced skeletal muscle hypertrophy in healthy, young men', Frontiers in Physiology 9, 1373. [doi]

## Pesevski, A., Franczak, B.C. and McNicholas, P.D. (2018), 'Subspace clustering with the multivariate-t distribution', Pattern Recognition Letters 112(1), 297-302. [doi]

## Punzo, A. Mazza, A. and McNicholas, P.D. (2018), 'ContaminatedMixt: An R package for fitting parsimonious mixtures of multivariate contaminated normal distributions', Journal of Statistical Software 85:10. [doi]

## Gallaugher, M.P.B. and McNicholas, P.D. (2018), 'Finite mixtures of skewed matrix variate distributions', Pattern Recognition 80, 83-93. [doi]

## Tang, Y, Browne, R.P. and McNicholas, P.D. (2018), 'Flexible clustering of high-dimensional data via mixtures of joint generalized hyperbolic distributions', Stat 7(1), e177. [doi]

## Andrews, J.L, Wickins, J.R., Boers, N.M. and McNicholas, P.D. (2018), 'teigen: An R package for model-based clustering and classification via the multivariate t distribution', Journal of Statistical Software 83:7. [doi]

## Skinnider, M.A., Dejong, C.A., Franczak, B.C., McNicholas, P.D. and Magarvey, N.A. (2017), 'Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm', Journal of Cheminformatics 9:46. [doi]

## Murray, P.M., Browne, R.P. and McNicholas, P.D. (2017), 'Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering', Journal of Multivariate Analysis 161, 141-156. [doi]

## Punzo, A. and McNicholas, P.D. (2017), 'Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model', Journal of Classification 34(2), 249-293. [doi]

## Murray, P.M., Browne, R.P. and McNicholas, P.D. (2017), 'A mixture of SDB skew-t factor analyzers', Econometrics and Statistics 3, 160-168. [doi]

## Gallaugher, M.P.B. and McNicholas, P.D. (2017), 'A matrix variate skew-t distribution', Stat 6(1), 160-170. [doi]

## Dang, U.J., Punzo, A., McNicholas, P.D., Ingrassia, S. and Browne, R.P. (2017), 'Multivariate response and parsimony for Gaussian cluster-weighted models', Journal of Classification 34(1), 4-34. [doi]

## Cheam, A.S.M., Marbac, M., and McNicholas, P.D. (2017), 'Model-based clustering for spatio-temporal data on air quality monitoring', Environmetrics 28(3), e2437. [doi]

## Wong, M.H.T., Mutch, D.M., and McNicholas, P.D. (2017), 'Two-way learning with one-way supervision for gene expression data', BMC Bioinformatics 18:150. [doi]

## Franczak, B.C., Castura, J.C., Browne, R.P., Findlay, C.J. and McNicholas, P.D. (2016), ‘Handling missing data in consumer hedonic tests arising from direct scaling: Imputation techniques for consumer hedonic tests', Journal of Sensory Studies 31(6), 514-523. [doi]

## Marbac ,M . and McNicholas, P.D. (2016), 'Dimension reduction in clustering', Wiley StatsRef: Statistics Reference Online. [doi]

## Tortora, C., McNicholas, P.D. and Browne, R.P. (2016) , 'A mixture of generalized hyperbolic factor analyzers', Advances in Data Analysis and Classification 10(4), 423-440. [doi]

## McNicholas, P.D. (2016), 'Model-based clustering', Journal of Classification 33(3), 331-373. [doi]

## Punzo, A. and McNicholas, P.D. (2016), 'Parsimonious mixtures of multivariate contaminated normal distributions', Biometrical Journal 58(6), 1506-1537. [doi]

## Punzo , A., Browne, R.P. and McNicholas, P.D. (2016), 'Hypothesis testing for mixture model selection', Journal of Statistical Computation and Simulation 86(14), 2797-2818. [doi]

## Azzalini, A., Browne, R.P., Genton, M.G. and McNicholas, P.D. (2016), 'On nomenclature for, and the relative merits of, two formulations of skew distributions', Statistics and Probability Letters 110, 201-206 [doi]

## Morris, K. and McNicholas, P.D. (2016), 'Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures', Computational Statistics and Data Analysis 97, 133-150. [doi]

## Cheam, A.S.M. and McNicholas, P.D. (2016), 'Modelling receiver operating characteristic curves using Gaussian mixtures', Computational Statistics and Data Analysis 93, 192-206. [doi]

## O’Hagan, A., Murphy, T.B., Gormley, I.C., McNicholas, P.D. and Karlis, D. (2016), ‘Clustering with the multivariate normal inverse Gaussian distribution’, Computational Statistics and Data Analysis 93, 18-30. [doi]

## Dang, U.J., Browne, R.P. and McNicholas, P.D. (2015), 'Mixtures of multivariate power exponential distributions', Biometrics 71(4), 1081-1089. [doi]

## Vrbik, I. and McNicholas, P.D. (2015), 'Fractionally-supervised classification', Journal of Classification 32(3), 359-381. [doi]

## Subedi, S., Punzo, A., Ingrassia, S. and McNicholas, P.D. (2015), 'Cluster-weighted t-factor analyzers for robust model-based clustering and dimension reduction', Statistical Methods and Applications 24(4), 623-649. [doi]

## Browne, R.P. and McNicholas, P.D. (2015), 'Multivariate sharp quadratic bounds via Σ-strong convexity and the Fenchel connection', Electronic Journal of Statistics 9(2), 1913-1938. [doi]

## Wei, Y. and McNicholas, P.D. (2015), 'Mixture model averaging for clustering', Advances in Data Analysis and Classification 9(2), 197-217. [doi]

## Browne, R.P. and McNicholas, P.D. (2015), 'A mixture of generalized hyperbolic distributions', Canadian Journal of Statistics 43(2), 176-198 . [doi]

## Franczak, B.C., Browne, R.P., McNicholas, P.D. and Findlay, C.J. (2015), 'Product Selection for Liking Studies: The Sensory Informed Design', Food Quality and Preference 44, 36-43. [doi]

## Franczak, B.C., Tortora, C., Browne, R.P. andMcNicholas, P.D. (2015), 'Unsupervised learning via mixtures of skewed distributions with hypercube contours', Pattern Recognition Letters 58(1), 69-76. [doi]

## Tang, Y., Browne, R.P. and McNicholas, P.D. (2015), ‘Model-based clustering of high-dimensional binary data’, Computational Statistics and Data Analysis 87, 84-101. [doi]

## Coneva, V., Simopoulos, C., Casaretto, J.A., El-kereamy, A., Guevara, D.R., Cohn, J., Zhu, T., Guo, L., Alexander, D.C., Bi, Y.-M., McNicholas, P.D. and Rothstein, S.J. (2014), ‘Metabolic and co-expression network-based analyses associated with nitrate response in rice’, BMC Genomics 15 :1056. [doi]

## Ralston, J., Badoud, F., Cattrysse, B., McNicholas, P.D. and Mutch, D.M. (2014), ‘Inhibition of stearoyl-CoA desaturase-1 in differentiating 3T3-L1 pre-adipocytes up-regulates Elongase 6 and down-regulates genes affecting triacylglycerol synthesis’, International Journal of Obesity 38, 1449-1456. [doi]

## Misyura, M., Guevara, D., Subedi, S., Hudson, D., McNicholas, P.D., Colasanti, J. and Rothstein, S.J. (2014), 'Nitrogen limitation and high density responses in rice suggest a role for ethylene in intraspecific competition', BMC Genomics 15:681. [doi]

## Andrews, J.L. and McNicholas, P.D. (2014), 'Variable selection for clustering and classification', Journal of Classification 31(2), 136-153. [doi]

## Franczak, B.C., Browne, R.P. and McNicholas, P.D. (2014), 'Mixtures of shifted asymmetric Laplace distributions', IEEE Transactions on Pattern Analysis and Machine Intelligence 36(6), 1149-1157. [doi]

## Browne, R.P. and McNicholas, P.D. (2014), 'Estimating common principal components in high dimensions', Advances in Data Analysis and Classification 8(2), 217-226. [doi]

## Subedi , S. and McNicholas, P.D. (2014), 'Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions', Advances in Data Analysis and Classification 8(2), 167-193. [doi]

## Murray, P.M., Browne, R.P. and McNicholas, P.D. (2014), 'Mixtures of skew-t factor analyzers', Computational Statistics and Data Analysis 77, 326-335. [doi]

## Murray, P.M., McNicholas, P.D. and Browne, R.P. (2014), 'A mixture of common skew-t factor analyzers', Stat 3(1), 68-82. [doi]

## Bhattacharya, S. and McNicholas, P.D. (2014), 'A LASSO-penalized BIC for mixture model selection', Advances in Data Analysis and Classification 8(1), 45-61. [doi]

## Lin, T.-I., McNicholas, P.D. and Hsiu, J.H. (2014), 'Capturing patterns via parsimonious t mixture models', Statistics and Probability Letters 88, 80-87. [doi]

## Browne, R.P. and McNicholas, P.D. (2014), 'Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models', Statistics and Computing 24(2), 203-210. [doi]

## Xia, Y. and McNicholas, P.D. (2014), 'A gradient method for the monotone fused least absolute shrinkage and selection operator', Optimization Methods and Software 29(3), 463-483. [doi]

## Vrbik, I. and McNicholas, P.D. (2014), 'Parsimonious skew mixture models for model-based clustering and classification', Computational Statistics and Data Analysis 71, 196-210. [doi]

## Morris, K., McNicholas, P.D. and Scrucca, L. (2013), 'Dimension reduction for model-based clustering via mixtures of multivariate t-distributions', Advances in Data Analysis and Classification 7(3), 321-338. [doi]

## Morris, K. and McNicholas, P.D. (2013), 'Dimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributions', Statistics and Probability Letters 83(9), 2088-2093. [doi] [erratum]

## Liseron-Monfils, C., Lewis, T., Ashlock, D., McNicholas, P.D., Fauteux, F., Stromvik, M. and Raizada, M.N. (2013), 'Promzea: A pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas', BMC Plant Biology 13:42. [doi]

## Andrews, J.L. and McNicholas, P.D. (2013), 'Using evolutionary algorithms for model-based clustering ', Pattern Recognition Letters 34(9), 987-992. [doi]

## Humbert, S., Subedi, S., Cohn, J., Zeng, B., Bi, Y.-M., Chen, X., Zhu, T., McNicholas, P.D., and Rothstein, S.J. (2013), 'Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses', BMC Genomics 14(3). [doi]

## Subedi, S., Punzo, A., Ingrassia, S. and McNicholas, P.D. (2013), 'Clustering and classification via cluster-weighted factor analyzers', Advances in Data Analysis and Classification 7(1), 5-40. [doi]

## Wong, M.H.T., Holst, C., Astrup, A., Handjieva-Darlenska, T., Jebb, S.A., Kafatos, A., Kunesova, M., Larsen, T.M., Martinez, D.M., Pfeiffer, A.F.H., van Baak, M.A., Saris, W.H.M., McNicholas, P.D. and Mutch, D.M. (2012), 'Caloric restriction induces changes in insulin and body weight measurements that are inversely associated with subsequent weight regain', PLoS ONE 7(8), e42858. [doi]

## Zulyniak, M.A., Ralston, J.C., Tucker, A.J., MacKay, K.A., Hillyer, L.M., McNicholas, P.D., Graham, T.E., Robinson, L.E., Duncan, A.M., Ma, D.W.L. and Mutch, D.M. (2012), 'Vaccenic acid in serum triglycerides is associated with markers of insulin resistance in men', Applied Physiology, Nutrition, and Metabolism 37(5), 1003-1007. [doi]

## Browne, R.P. and McNicholas, P.D. (2012), 'Model-based clustering and classification of data with mixed type', Journal of Statistical Planning and Inference 142(11), 2976-2984. [doi]

## Vrbik, I. and McNicholas, P.D. (2012), 'Analytic calculations for the EM algorithm for multivariate skew-t mixture models', Statistics and Probability Letters 82(6), 1169-1174. [doi]

## McNicholas, P.D. and Subedi, S. (2012), 'Clustering gene expression time course data using mixtures of multivariate t-distributions', Journal of Statistical Planning and Inference 142(5), 1114-1127. [doi]

## Feng, Z.Z., Yang, X., Subedi, S. and McNicholas, P.D. (2012), 'The LASSO and sparse least squares regression methods for SNP selection in predicting quantitative traits', IEEE Transactions on Computational Biology and Bioinformatics 9(2), 629-636. [doi]

## Browne, R.P., McNicholas, P.D. and Sparling, M.D. (2012), 'Model-based learning using a mixture of mixtures of Gaussian and uniform distributions', IEEE Transactions on Pattern Analysis and Machine Intelligence 34(4), 814-817. [doi]

## Andrews, J.L. and McNicholas, P.D. (2012), 'Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions', Statistics and Computing 22(5), 1021-1029. [doi]

## Steane, M.A., McNicholas, P.D. and Yada, R. (2012), 'Model-based classification via mixtures of multivariate t-factor analyzers’, Communications in Statistics -- Simulation and Computation 41(4), 510-523. [doi]

## McNicholas, P.D. (2011), 'On model-based clustering, classification, and discriminant analysis', Journal of the Iranian Statistical Society 10(2), 181-199.

## Xu, R., McNicholas, P.D., Desmond, A.F. and Darlington, G.A. (2011), 'A first passage time model for long term survivors with competing risks', The International Journal of Biostatistics 7(1), Article 26. [doi]

## Andrews, J.L. and McNicholas, P.D. (2011), 'Mixtures of modified t-factor analyzers for model-based clustering, classification, and discriminant analysis', Journal of Statistical Planning and Inference 141(4), 1479-1486. [doi]

## Andrews, J.L. and McNicholas, P.D. (2011), 'Extending mixtures of multivariate t-factor analyzers', Statistics and Computing 21(3), 361-373. [doi]

## Andrews, J.L., McNicholas, P.D. and Subedi, S. (2011), 'Model-based classification via mixtures of multivariate t-distributions', Computational Statistics and Data Analysis 55(1), 520-529. [doi]

## Balka, J., Desmond, A.F. and McNicholas, P.D. (2011), 'Bayesian and likelihood inference for cure rates based on defective inverse Gaussian regression models', Journal of Applied Statistics 38(1), 127-144. [doi]

## McNicholas, P.D. and Murphy, T.B. (2010), 'Model-based clustering of microarray expression data via latent Gaussian mixture models', Bioinformatics 26(21), 2705-2712. [doi] [data]

## Shaikh, M., McNicholas, P.D. and Desmond, A.F. (2010), 'A pseudo-EM algorithm for clustering incomplete longitudinal data', The International Journal of Biostatistics 6(1), Article 8. [doi]

## McNicholas, P.D. and Murphy, T.B. (2010), 'Model-based clustering of longitudinal data', The Canadian Journal of Statistics 38(1), 153-168. [doi]

## McNicholas, P.D. (2010), 'Model-based classification using latent Gaussian mixture models', Journal of Statistical Planning and Inference 140(5), 1175-1181. [doi]

## McNicholas, P.D., Murphy, T.B., McDaid, A.F. and Frost, D. (2010), 'Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models', Computational Statistics and Data Analysis 54(3), 711-723. [doi]

## Balka, J., Desmond, A.F. and McNicholas, P.D. (2009), 'Review and implementation of cure models based on first hitting times for Wiener processes', Lifetime Data Analysis 15(2), 147-176. [doi]

## Fu, Y., Kim, L.-T. and McNicholas, P.D. (2009), 'Changes on enological parameters of white wine packaged in bag-in-box during secondary shelf life’, Journal of Food Science 74(8), C608-C618. [doi]

## McNicholas, P.D. and Murphy, T.B. (2008), 'Parsimonious Gaussian mixture models', Statistics and Computing 18(3), 285-296. [doi]

## McNicholas, P.D., Murphy, T.B. and O'Regan, M. (2008), 'Standardising the lift of an association rule', Computational Statistics and Data Analysis 52(10), 4712-4721. [doi]

## McNicholas, P.D. (2007), 'Association rule analysis of CAO data (with discussion)', Journal of the Statistical and Social Inquiry Society of Ireland 36, 44-83. [edepositIreland]

## Ahmad, K., Rogers, S., McNicholas, P.D. and Collins P. (2007), 'Narrowband UVB and PUVA in the treatment of mycosis fungoides: A retrospective study', Acta Dermato-Venereologica 87(5), 413-417. [doi]

## Opinion/Editorial

McNicholas, P.D. (2019), 'Data science', FACETS 4(1), 131-135. [doi]

## McNicholas, P.D. (2016), 'Turning the spit: A perspective on the NSERC Discovery Grant review process', Liaison 30(4), 45-55. [pdf]

## Proceedings & Book Chapters

## Gallaugher, M.P.B. and McNicholas, P.D. (2020), ‘Parsimonious mixtures of matrix variate bilinear factor analyzers’ in T. Imaizumi et al. (eds.), Advanced Studies in Behaviormetrics and Data Science: Essays in Honor of Akinori Okada, Springer: Singapore, pp. 177-196. [doi]

## McNicholas, S.M., McNicholas,P.D., and Browne, R.P. (2017), ‘A mixture of variance-gamma factor analyzers’. In Ahmed, S.E., editor, Big and Complex Data Analysis. Cham: Springer International Publishing, pp. 369-385. [doi]

## Dang, U.J and McNicholas, P.D. (2015), Families of parsimonious finite mixtures of regression models. In: Morlini, I., Minerva T. and Vichi, M., editors, Advances in Statistical Models for Data Analysis , Studies in Classification, Data Analysis, and Knowledge Organization. Cham: Springer International Publishing, pp. 73-84. [doi]

## McNicholas, P. D. (2013). On clustering and classification via mixtures of multivariate t-distributions. In Guidici, P., Ingrassia, S. and Vichi, M., editors, Statistical Models for Data Analysis , Studies in Classification, Data Analysis, and Knowledge Organization. Cham: Springer International Publishing, pp. 233-240. [doi]

## Browne, R.P. and McNicholas, P. D. (2013), Mixture and latent class models in longitudinal and other settings. In Scott, M.A .,Simonoff, J.S., and Marx, B.D., editors, The SAGE Handbook of Multilevel Modelling. SAGE Publications Ltd., pp.357- 370.

## Ashlock, D., Schonfeld, J. and McNicholas, P.D. (2011), 'Translation tables: A genetic code in a evolutionary algorithm'. In IEEE Congress on Evolutionary Computation (CEC), New Orleans, pp. 2685-2692. [doi]

## McNicholas, P.D. and Zhao, Y.C. (2009), ‘Association rules: An overview’, in Y. Zhao, C. Zhang and L. Cao, editors, Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, IGI Global, pp. 1-10.

## Discussions of Journal Articles

## McNicholas, P.D., McNicholas, S.M. and Tait, P.A. (2018), Discussion of 'Statistical challenges of administrative and transaction data’ by Hand, Journal of the Royal Statistical Society: Series A 181(3), 594-595. [doi]

## Gallaugher, M.P.B. and McNicholas, P.D. (2017), Discussion of 'Random-projection ensemble classification’ by Cannings and Samworth, Journal of the Royal Statistical Society: Series B 79(4), 1011-1012. [doi]

## McNicholas, P.D. and Subedi, S. (2016), Discussion of 'Perils and potentials of self-selected entry to epidemiological studies and surveys' by Keiding ad Louis, Journal of the Royal Statistical Society: Series A 179(2), 362-363. [doi]

## Subedi, S. and McNicholas, P.D. (2015), Discussion of 'Analysis of forensic DNA mixtures with artefacts' by Cowell et al., Journal of the Royal Statistical Society: Series C 64(1), 43-44. [doi]

## McNicholas, P.D., Browne, R.P. and Murray, P.M. (2013), Discussion of 'Model-based clustering and classification with non-normal mixture distributions' by Lee and McLachlan, Statistical Methods and Applications 22(4), 467-472. [doi]

## McNicholas, P.D. and Browne, R.P. (2013), Discussion of 'How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification' by Hennig and Liao, Journal of the Royal Statistical Society: Series C 62(3), 352-353. [doi]