Research Interests

Check out my current research statement to get a feel for what I spend most of my time on.


  • Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2022). Exponential Random Graph Models for Dynamic Signed Networks: An Application to International Relations. ArXiv.



  • De Nicola, G., Fritz, C., Mehrl, M., & Kauermann, G. (2023). Dependence matters: Statistical models to identify the drivers of tie formation in economic networks. Journal of Economic Behavior & Organization, 215, 351–363.
  • Schweinberger, M., & Fritz, C. (2023). Discussion of “A tale of two datasets: Representativeness and generalisability of inference for samples of networks” by Pavel N. Krivitsky, Pietro Coletti, and Niel Hens. Journal of the American Statistical Association, (OnlineFirst), 1–5.
  • Fritz, C., De Nicola, G., Kevorg, S., Harhoff, D., & Kauermann, G. (2023). Modelling the large and dynamically growing bipartite network of German patents and inventors. Journal of the Royal Statistical Society. Series A (Statistics in Society), 186(3), 557–576.
  • Rügamer, D., Kolb, C., Fritz, C., Pfisterer, F., Bischl, B., Shen, R., Bukas, C., de Andrade e Sousa, L. B., Thalmeier, D., Baumann, P., Klein, N., & Müller, C. L. (2023). deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. Journal of Statistical Software, 105(2), 1–31.
  • Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2023). All that Glitters is not Gold: Relational Events Models with Spurious Events. Network Science, 11(SI 2).


  • Fritz, C., De Nicola, G., Rave, M., Weigert, M., Berger, U., Küchenhoff, H., & Kauermann, G. (2022). Statistical modelling of COVID-19 data: Putting Generalised Additive Models to work. Statistical Modelling, (OnlineFirst).
  • Fritz, C., De Nicola, G., Günther, F., Rügamer, D., Rave, M., Schneble, M., Bender, A., Weigert, M., Brinks, R., Hoyer, A., Berger, U., Küchenhoff, H., & Kauermann, G. (2022). Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. Journal of Computational and Graphical Statistics, to appear.
  • Fritz, C., Dorigatti, E., & Rügamer, D. (2022). Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. Scientific Reports, 3930(12), 1–18.
  • Berger, U., Fritz, C., & Kauermann, G. (2022). Reihentestungen an Schulen können die Dunkelziffer vonCOVID-19 Infektionen unter Schülern signifikant senken. Das Gesundheitswesen.
  • Fritz, C., & Kauermann, G. (2022). On the Interplay of Regional Mobility, Social Connectedness, and the Spread of COVID-19 in Germany. Journal of the Royal Statistical Society. Series A (Statistics in Society), 185(1).
  • Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2022). The Role of Governmental Weapons Procurements in Forecasting Monthly Fatalities in Intrastate Conflicts: A Semiparametric Hierarchical Hurdle Model. International Interactions, 8(4), 778–799.


  • Fritz, C., Thurner, P. W., & Kauermann, G. (2021). Separable and Semiparametric Network-based Counting Processes applied to the International Combat Aircraft Trades. Network Science, 9(3), 291–311.


  • Baumann, S. A., Fritz, C., & Mueller, R. S. (2020). Food antigen-specific IgE in dogs with suspected food hypersensitivity. Tierarztliche Praxis. Ausgabe K, Kleintiere/Heimtiere, 48(6), 395–402.
  • Fritz, C., Lebacher, M., & Kauermann, G. (2020). Tempus Volat, Hora Fugit: A survey of Tie‐oriented Dynamic Network Models in Discrete and Continuous Time. Statistica Neerlandica, 74(3), 275–299.