Advances in Compositional Data Analysis

Preface.- J.J. Egozcue and W.L. Maldonado: An interpretable orthogonal decomposition of positive square matrices.- Part I Fundamentals.- I. Erb and N. Ay: The information-geometric perspective of compositional data analysis.- D.R. Lovell: Log-ratio analysis of finite precision data: caveats, and connections to digital lines and number theory.- G. Mateu-Figueras, G.S. Monti and J.J. Egozcue: Distributions on the simplex revisited.- J. Graffelman: Compositional biplots: a story of false leads and hidden features revealed by the last dimensions.- Part II Statistical Methodology.- K. Fačevicová, P. Kynčlová and K. Macků Geographically weighted regression analysis for two-factorial compositional data.- C. Barceló-Vidal and J.A. Martín-Fernández: Factor analysis of compositional data with a total.- M. Gallo, V. Simonacci and V. Todorov: A compositional three-way approach for student satisfaction analysis.- M. Templ: Artificial neural networks to impute rounded zeros in compositional data.- E. Saus-Sala, À. Farreras-Noguer, N. Arimany-Serrat, and G. Coenders: Compositional du pont analysis. A visual tool for strategic financial performance assessment.- A. Menafoglio: Spatial statistics for distributional data in Bayes spaces: from object-oriented kriging to the analysis of warping functions.- C. Thomas-Agnan, T. Laurent, A. Ruiz-Gazen, N. Thi Huong An, R. Chakir and A. Lungarska: Spatial simultaneous autoregressive models for compositional data: application to land use.- Part III Applications.- A. Buccianti, C. Gozzi: The whole versus the parts: the challenge of compositional data analysis (CoDA) methods for geochemistry.- M.A. Engle and J.A. Chaput: Groundwater origin determination in historic chemical datasets through supervised compositional data analysis: Brines of the Permian Basin, USA.- J.M. McKinley, U. Mueller, P.M. Atkinson, U. Ofterdinger, S.F. Cox, R. Doherty, D. Fogarty and J.J. Egozcue.- Chronic kidney disease of uncertain aetiology and its relation with waterborne environmental toxins: An investigation via compositional balances.- R.A. Olea, J.A. Martín-Fernández and W.H. Craddock: Multivariate classification of the crude oil petroleum systems in southeast Texas, USA, using conventional and compositional data analysis of biomarkers.- J.R. Wu, J.M. Macklaim, B.L. Genge and G.B. Gloor: Finding the centre: compositional asymmetry in high-throughput sequencing datasets.- L. Huang and H. Li: Bayesian balance-regression in microbiome studies using stochastic search.- D.E. McGregor, P.M. Dall, J. Palarea-Albaladejo and S.F.M. Chastin: Compositional data analysis in physical activity and health research. Looking for the right balance.- D. Dumuid, Z. Pedisic, J. Palarea-Albaladejo, J.A. Martín-Fernández, K. Hron and T. Olds: Compositional data analysis in time-use epidemiology.

Peter Filzmoser is a Professor of Statistics at the Vienna University of Technology, Austria, where he received his PhD and postdoctoral lecture qualification. He has been a Visiting Professor in Toulouse, France and Minsk, Belarus. Furthermore, he has authored more than 200 research articles and several R packages and has co-authored books on compositional data analysis (Springer, 2018), on multivariate methods in chemometrics (CRC Press, 2009) and on analysing environmental data (Wiley, 2008).

Karel Hron is a Professor at Palacký University in Olomouc, Czech Republic. He holds a PhD in Applied Mathematics from the same university. His research chiefly focuses on the statistical analysis of compositional data (CoDa) and applications of CoDa methodologies. Further, he has developed methods for CoDa that have since been implemented in the R software. He has authored more than 100 research articles and co-authored a book on compositional data analysis (Springer, 2018).

Josep Antoni Martín-Fernández is a Professor at the Department of Computer Science, Applied Mathematics and Statistics, University of Girona, Spain. His interests primarily lie in the analysis of compositional data (CoDa), and he has released more than one hundred publications related to the topic. He is the principal investigator of the CoDa Research Group, where he pursues publicly funded research projects on CoDa. He has also taught many CoDa courses.

Dr Javier Palarea-Albaladejo is a Principal Statistical Scientist at Biomathematics and Statistics Scotland, Edinburgh, UK, where he provides high-level statistical inputs for interdisciplinary scientific research, and engages in methodological research and training with a focus on multivariate and compositional data analysis. He has published over 70 peer-reviewed articles in scientific journals, created two R packages, and led the statistical component of grants supported by various funders.