Glenna Nightingale née Evans

Computational Social Science

Our vision is to apply statistical modeling approaches within a robust computational environment to address research questions within demography and health in social science.  At the moment the methods employed include Generalised additive models and Latent Mixture models.  We are currently preparing a manuscript which features GAMs and the use of the UKHLS data.  Some of us will be working on an obesity project which starts in August 2023.

  • R Shiny apps to teach statistics in R. 
  • R packages to facilitate point process analyses.  I have recently constructed an R package "zoi" which facilitates the calculation of the area of non-overlap
  • Developing point process methods
  • Grant applications which require innovative statistical methods

Statistical computing, Statistical methods

Let's collaborate!


Computational Spatial Statistics

The vision for this strand of research is to apply spatial statistical methods to research questions in ecology, epidemiology, and public health.  At the moment the methods used include point process models, namely, Area interaction point processes and Log Gaussian Cox point processes

Dr Glenna Nightingale