~ Bayesian statistics ~

Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 9(1). https://doi.org/10.1214/14-AOAS788

Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573–603. https://doi.org/10.1037/a0029146

Bååth, R., (2014) Bayesian First Aid: A Package that Implements Bayesian Alternatives to the Classical *.test Functions in R. In the proceedings of UseR! 2014 – the International R User Conference.

Bürkner, P.-C., Scholz, M., & Radev, S. T. (2022). Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy. Statistics Surveys. https://doi.org/10.1214/23-SS145

~ “Covid” Mortality ~

Rockenfeller, R., Günther, M., & Mörl, F. (2023). Reports of deaths are an exaggeration: all-cause and NAA-test-conditional mortality in Germany during the SARS-CoV-2 era. Royal Society Open Science, 10(8). https://doi.org/10.1098/rsos.221551

Robinson, L. A., Sullivan, R., & Shogren, J. F. (2020). Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand | Faculty of Medicine | Imperial College London. Risk Analysis.

Eubank, S., Eckstrand, I., Lewis, B., Venkatramanan, S., Marathe, M., & Barrett, C. L. (2020). Commentary on Ferguson, et al., “Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand.” Bulletin of Mathematical Biology, 82(4), 52. https://doi.org/10.1007/s11538-020-00726-x

Paglino, E., Lundberg, D. J., Wrigley-Field, E., Zhou, Z., Wasserman, J. A., Raquib, R., Chen, Y.-H., Hempstead, K., Preston, S. H., Elo, I. T., Glymour, M. M., & Stokes, A. C. (2024). Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths. Proceedings of the National Academy of Sciences, 121(6). https://doi.org/10.1073/pnas.2313661121

Cronin, C. J., & Evans, W. N. (2021). Excess mortality from COVID and non-COVID causes in minority populations. Proceedings of the National Academy of Sciences, 118(39). https://doi.org/10.1073/pnas.2101386118

~ Embodied cognition & mathematics ~

Fischer, M. H. (2012). A hierarchical view of grounded, embodied, and situated numerical cognition. Cognitive Processing, 13(S1), 161–164. https://doi.org/10.1007/s10339-012-0477-5

Winter, B., & Yoshimi, J. (2020). Metaphor and the Philosophical Implications of Embodied Mathematics. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.569487

~ Philosophy of science ~

Shanks, D. R. (1985). Hume on the Perception of Causality. Hume Studies, 11(1), 94–108. https://doi.org/10.1353/hms.2011.0025

~ R ~

Chang W., Cheng J., Allaire J. J., Xie Y., McPherson J. (2019). shiny: Web application framework for R (R package Version 1.3.1) [Computer software]. Retrieved from https://CRAN.R-project.org/package=shiny