Software resources by DARE members and collaborators.

• Methods
  • Clustering
  • Graphical Models
  • Machine Learning
  • MCMC
  • Variational Bayes
  • Visualisation
• Projects
  • Biodiversity
  • Health
  • Minerals
  • Water
  • Miscellaneous
• External Resources

• DBSCAN Parameter Estimation for Sklearn | Python: Low-cost Pareto-like parameter estimation for sklearn's DBSCAN clustering algorithm.

• Bayesian Networks | C++: Modelling and inference using Bayesian Networks.

• Bushfires ML Analysis | Google Earth Engine, Python, R: Analysis of bushfire data using multiple methodologies for predicting and understanding bushfire patterns.
• Flax Model Hybridisation | Python: A ML-model hybridisation example in JAX and Flax.

Public

• Adaptive MCMC Parallelisation in Stan | R, Stan: Dynamic determination of optimum chains and cores for MCMC.
• Bayesian Neural Networks with MCMC Tutorial | Python: Code to accompany a Bayesian neural networks via MCMC tutorial.
• Benchmarking Stan under Dynamic Overclocking | R, Stan: Stan benchmarking repeatability under dynamic overclocking.
• Riemann: A Research Framework for MCMC | Python: Advanced MCMC methods for sampling complex, high-dimensional posterior distributions.

Private

• Introductory MCMC tutorial | (private) Python: Intro to MCMC tutorial.

Public

• VBLab: A MATLAB Package for Variational Inference | MATLAB: A probabilistic programming package for automatic variational Bayesian inference on both common, pre-defined statistical models and user-defined models.

Private

• Ranking VAE | (private) Python: Applying variational autoencoders to rank aggregation.

Public

• ggplot2 LaTeX | R: Styling R ggplot2 graphics with LaTeX.
• Java Plotly | Java: Example Plotly graph generation from Java.
• Stata LaTeX | Stata: Styling Stata graphics with LaTeX.
• TikZ DAGs | R, TikZ: Semi-automated TikZ directed acyclic graphs in R.
• TikZ Raster Graphics | LaTeX, TikZ: Example raster graphics.

Private

• Easy Leaflet Plot | (private) Python: Plotting data on interactive web maps with a DARE template.
• GeoVisual | (private) JavaScript, Python: Visualising continuous density functions over a geographical map.

Public

• Fish Stock Simulation | R: Simulating population trajectories for model testing.
• Fisheries Comparison | R: Repository for data-limited catch-only stock assessment methods.

Private

• Ecological Population Abundances | (private) R: Missing value imputation for ecological population abundances.
• Fish Biomass I | (private) R: Fish biomass estimation models.
• Fish Biomass II | (private) R: Fish biomass estimation using the data-limited CMSY method.
• Global Fishing Index 2025 | (external, private) R: Global fishing sustainability, Minderoo Foundation repo.

Public

• COVID-19 Models Evaluation: IHME | Python: Evaluating COVID-19 predictive models by the Institute for Health Metrics and Evaluation.
• COVID-19 Models Evaluation: Imperial | R, Stan: Comparison of models by Imperial College's COVID-19 Response Team.
• COVID-19 Models Evaluation: New York | R: Evaluating COVID-19 daily deaths and ICU bed utilisation predictions in New York.
• Memory Modelling | R: Modelling working memory dynamics in individuals engaging in a stimulus-tracking n-back experiment.

Private

• COVID-19 Models Evaluation: Supplemental | (private) JavaScript, Python, R: Evaluating COVID-19 daily deaths and ICU bed utilisation predictions.
• COVID-19: Mixture Model | (private) Python: Mixture model based on Kalman filtering.

Public

• gPhone Gravity Meter Data Parser | R: A data parser for the gPhone 74 gravity meter.
• Soil Spectral Inference with Julia | Julia: A Julia version of the R soilspec package (tools for soil spectral inference). Work in progress.

Private

• Soil Biome and Geochemical Signatures | (private) Python: Exploring Geoscience Australia soils data from southwestern Victoria.

Public

• CMIP Tools | Python: CMIP data tools for the Artemis cluster (retired in 2025).
• JAX GR4J | Python: JAX implementation of the GR4J hydrological model.
• Llara Soil Moisture Probes | Python: Modelling Llara farm soil moisture probes data.
• Streamflow Trends: Climate Change | C++, R, shell: Disentangling climate change trends in Australian streamflow.

Private

• Aquifer Layers | (private) Python: Aquifer layer identification via DBSCAN and related clustering methods.
• DARE Hydrology | (private) Python: Mining impacts on groundwater (via Mixture of Experts).
• Dynamic Rating Curve Fitting | (private) Python: Fitting streamflow rating curves.
• ENSO | (private) R: El Niño-Southern Oscillation (ENSO) project.
• Floodplain Harvesting Compliance | (private) Python: NRAR floodplain harvesting compliance project.
• Lihir Water | (private) Python, R: Optimising water consumption for sustainable mining on Lihir Island.
• NSW Rainfall | (private) MATLAB, R: NSW rainfall analysis.
• Streamflow Trends: WaterNSW | (private) Python, Stan: Code for the streamflow trends project.
• WIATW | (private) Python: Code repository for the WIATW (Where Is All The Water?) project. This project uses mostly GP methods to quantify uncertainty over space and time in the components of the water balance.

Public

• DARE CSS | CSS: DARE-themed stylesheets.
• DARE Data Challenge: Bayes on the Beach | Resources for DARE's R and Stan-based Bayes on the Beach 2024 Challenge.
• DARE Data Challenge: Internal | Python, R: Resources for the DARE internal deluxe data challenge.
• DARE Data Challenge: Newcrest | Python, R: Resources for the DARE-Newcrest data challenge.
• DARE Poster Template | CSS: DARE-themed academic poster template using Marp.
• Gaussian Combiner | MATLAB: Combining multiple univariate Gaussians into one multivariate Gaussian.
• Nimbus Guide | A guide to using the legacy Nimbus Research Cloud Service (discontinued in 2025).
• UBC-GIF to netCDF | Python: A script to convert UBC-GIF data (e.g., this GA data) to netCDF.

Private

• DARE Trivia Team Generator | (private) Excel VBA: Automatic trivia teams creation in Excel.
• NumPyro Tutorial | (private) Python: DARE NumPyro tutorial (FAHH) code.
• SIMAGRI | (private) Fortran, Python: SIMAGRI Agricultural Simulator.

• Machine learning @ Transitional Artificial Intelligence Research Group (UNSW, Sydney). Machine learning, neuro-evolution, optimisation and Bayesian inference. Code resources developed by DARE CI Rohitash Chandra's group at UNSW.