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Under review

Disclaimer

The following resources are being reviewed, there is no warranty about their quality nor do we consider them worth mentioning on the list.

Resource list

  • Amat Rodrigo, Joaquin, and Javier Escobar Ortiz. Skforecast. 0.11.0, 2023, doi:10.5281/zenodo.8382788. [Link]
  • ArcticDB. ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem. [Link]
  • Awesome Production Machine Learning. A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. [Link]
  • Barandas, Marília, et al. "TSFEL: Time series feature extraction library." SoftwareX 11 (2020): 100456. [Paper] [Code]
  • Bendersky, Eli. Variance of the sum of independent random variables. Eli Bendersky's website, 2009. [Link]
  • Bonabeau, Eric. "Agent-based modeling: Methods and techniques for simulating human systems." Proceedings of the national academy of sciences 99.suppl_3 (2002): 7280-7287. [Link]
  • Ceja, Enrique Garcia. Behavior analysis with machine learning using R. Chapman and Hall/CRC, 2021.
  • Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M. & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI [Link]
  • Dagster. An orchestration platform for the development, production, and observation of data assets. [Link]
  • de Gooijer, Stijn. Poetry Guide: Guide on how to use Poetry for your projects. [Link]
  • Delatte, Hugo, and Carlo Nicolini. Skfolio. [Link]
  • diffstatic. A structural diff tool that understands syntax. [Link]
  • DuckDB. DuckDB is an in-process SQL OLAP Database Management System. [Link]
  • Fabisch, Alexander. "gmr: Gaussian mixture regression." Journal of Open Source Software 6.62 (2021): 3054. [Link]
  • Feng, Ling, et al. "Linking agent-based models and stochastic models of financial markets." Proceedings of the National Academy of Sciences 109.22 (2012): 8388-8393. [Link]
  • Fugue. A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites. [Link]
  • Iurii D. Katser and Vyacheslav O. Kozitsin, “Skoltech Anomaly Benchmark (SKAB).” Kaggle, 2020, doi: 10.34740/KAGGLE/DSV/1693952. [Link]
  • Kristensen, Laura, and Anton Vorobets. "Portfolio Optimization and Parameter Uncertainty." Available at SSRN (2024). [Link]
  • Hasz, Brendan. Bayesian Gaussian Mixture Modeling with Stochastic Variational Inference. Brendan Hasz, 2019. [Link]
  • Hasz, Brendan. Probflow: A Python package for building Bayesian models with TensorFlow or PyTorch. [Link]
  • Herman, Michael. Deploying and Hosting a Machine Learning Model with FastAPI and Heroku. Testdriven.io, 2023. [Link]
  • Herzen, Julien, et al. "Darts: User-friendly modern machine learning for time series." Journal of Machine Learning Research 23.124 (2022): 1-6. [Paper] [Code]
  • Johnson, Geoffrey. Featured posts on Statistics. LinkedIn. [Link]
  • Larsen, Nicholas, et al. "Statistical challenges in online controlled experiments: A review of a/b testing methodology." The American Statistician (2023): 1-15.00
  • Lempert, Robert. "Agent-based modeling as organizational and public policy simulators." Proceedings of the national academy of sciences 99.suppl_3 (2002): 7195-7196. [Link]
  • Mayo, D. G., & Hand, D. (2022). Statistical significance and its critics: practicing damaging science, or damaging scientific practice?. Synthese, 200(3), 220. [Link]
  • Moseley, Ben. So, what is a physics-informed neural network?. Ben Moseley, 2021. [Link]
  • Parasurama, Prasanna. Why Overlapping Confidence Intervals mean Nothing about Statistical Significance. Towards Data Science, 2017. [Link]
  • Patacchiola, Massimiliano. Evidence, KL-divergence, and ELBO. root@mpatacchiola:~$, 2021. [Link]
  • Pauli, Francesco. "The p-value Case, a Review of the Debate: Issues and Plausible Remedies." Studies in Theoretical and Applied Statistics: SIS 2016, Salerno, Italy, June 8-10 (2018): 95-104. [Link]
  • Pybroker. Algorithmic Trading in Python with Machine Learning. [Link]
  • Pyrcz, Michael J. PythonNumericalDemos: Educational Data Science Demonstrations Repository. [Link]
  • Qlib. An AI-oriented quantitative investment platform by Microsoft. [Link]
  • Recht, Ben. From Intervals to Bands. Arg min, 2024. [Link]
  • Sales De Andrade, Eric. How To Test And Build Python Packages With Pytest, Tox And Poetry. PyTest With Eric, 2023. [Link]
  • Sportisse, Aude, Claire Boyer, and Julie Josse. "Imputation and low-rank estimation with missing not at random data." Statistics and Computing 30.6 (2020): 1629-1643. [Paper] [Code]
  • Stutz, David, et al. "Conformal prediction under ambiguous ground truth." arXiv preprint arXiv:2307.09302 (2023). [Paper] [Code]
  • Stutz, David, et al. "Evaluating AI systems under uncertain ground truth: a case study in dermatology." arXiv preprint arXiv:2307.02191 (2023). [Paper] [Code]
  • Stutz, David. On the Utility of Conformal Prediction Intervals. I Am David Stuz, 2024. [Link]
  • Sumpter, David. Four Ways of Thinking: Statistical, Interactive, Chaotic and Complex. [Link]
  • SWE-agent: Agent Computer Interfaces Enable Software Engineering Language Models. Princeton NLP. [Link]
  • Tohme, Tarek, and William Bialek. "A brief tutorial on information theory." arXiv preprint arXiv:2402.16556 (2024). [Link]
  • tsfresh. Automatic extraction of relevant features from time series. [Link]
  • Vanderbroucke, Bert. Memory mapping files. Bert's blog, 2019. [Link]
  • Wang, Zhuang, Koby Crammer, and Slobodan Vucetic. "Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale svm training." The Journal of Machine Learning Research 13.1 (2012): 3103-3131. [Link]
  • Wenger, Jonathan, Nicholas, Krämer, Marvin, Pförtner, Jonathan, Schmidt, Nathanael, Bosch, Nina, Effenberger, Johannes, Zenn, Alexandra, Gessner, Toni, Karvonen, François-Xavier, Briol, Maren, Mahsereci, Philipp, Hennig. "ProbNum: Probabilistic Numerics in Python." (2021). [Link]
  • Yan, Ziyou. (Feb 2024). Don't Mock Machine Learning Models In Unit Tests. eugeneyan.com. [Link]
  • Zhao, Yue, Zain Nasrullah, and Zheng Li. "Pyod: A python toolbox for scalable outlier detection." Journal of machine learning research 20.96 (2019): 1-7. [Paper] [Code]