TRR 391 Software
Please find below all software packages developed and published within TRR 391.
BayesFlow is a Python library for efficient Bayesian inference with deep learning. It provides users with:
- A user-friendly API for amortized Bayesian workflows
- A rich collection of neural network architectures
- Multi-backend support via Keras3: You can use PyTorch, TensorFlow, or JAX
References:
SChangeBlock is an R package that provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence.
References:
- Görz, S., Fried, R. (2025). Detecting changes in the mean of spatial random fields on a regular grid. arXiv. DOI.
- Görz, S., Fried, R. (2026). SChangeBlock: Spatial structural change detection by an analysis of variability between blocks of observations. R package. CRAN.
- GitHub
External references:
- Schmidt, S. K. (2024). Detecting changes in the trend function of heteroscedastic time series. Bernoulli 30 (4), 2598–2622. DOI.
