Shape analysis; object oriented data analysis; manifold-valued data analysis; functional data analysis; networks; spatial statistics and statistical image analysis.


Some recent publications

  • Severn, K. E., Dryden, I. L. and Preston, S. P. (2022). Manifold valued data analysis of samples of networks, with applications in corpus linguistics. Annals of Applied Statistics. To appear.
  • Seymour, R.G., Sirl, D., Preston, S.P., Dryden, I.L., Ellis, M.J.A, Perrat, B. and Goulding, J. (2021). The Bayesian spatial Bradley–Terry model: urban deprivation modelling in Tanzania. Journal of the Royal Statistical Society, Series C (Applied Statistics)
  • Severn, K. E., Dryden, I. L. and Preston, S. P. (2021). Non-parametric regression for networks. Stat 10, e373
  • Kim, K., Dryden, I.L., Le, H. and Severn, K.E. (2021). Smoothing splines on Riemannian manifolds, with applications to 3D shape space. Journal of the Royal Statistical Society, Series B (Methodology). 83, 108–132.
  • Dryden, I.L., Kume, A., Paine, P.J. and Wood, A.T.A. (2021). Regression modelling for size-and-shape data based on a Gaussian model for landmarks. Journal of the American Statistical Association 116, 1011–1022.
  • Dryden, I.L. (2021). shapes package, R Foundation for Statistical Computing, Vienna, Austria, Contributed package. Version 1.2.6, March 31, 2021.
  • Louison, K. A., Dryden, I. L., and Laughton, C. A. (2021). GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling. Journal of Chemical Theory and Computation 17, 12, 7930–7937
  • Dryden, I.L., Kim, K., Laughton, C.A. and Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13, 2213–2234.
  • Dryden, I.L., Kim, K. and Le, H. (2019). Bayesian linear size-and-shape regression with applications to face data. Sankhya A, 81, 83–103.

Links to citations and technical reports

Google Scholar


shapes software

shapes demo