The DOI matches its Crossref record.
Statsmodels: Econometric and Statistical Modeling with Python
Open accessAbstract
Statsmodels is a library for statistical and econometric analysis in Python.This paper discusses the current relationship between statistics and Python and open source more generally, outlining how the statsmodels package fills a gap in this relationship.An overview of statsmodels is provided, including a discussion of the overarching design and philosophy, what can be found in the package, and some usage examples.The paper concludes with a look at what the future holds.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Items 101–127 of 127
Adise, S., Marshall, A. T., Kan, E., Gonzalez, M. R., & Sowell, E. R. (2023) · Health Psychology · doi:10.1037/hea0001250 · Cited by 15
Ultrastructural analysis of wild-type and RIM1α knockout active zones in a large cortical synapse
Lichter, K., Paul, M. M., Pauli, M., Schoch, S., Kollmannsberger, P., Stigloher, C., Heckmann, M., & Sirén, A.-L. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111382 · Cited by 15
Face Recognition in Children: A Longitudinal Study
Bahmani, K., & Schuckers, S. (2022) · 2022 International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf55382.2022.9794551 · Cited by 15
A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes
Huang, Y., Guo, J., Donahoo, W. T., Lee, Y. A., Fan, Z., Lu, Y., Chen, W.-H., Tang, H., Bilello, L., Saguil, A. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52960-9 · Cited by 14
Hazelton, J. L., Della Bella, G., Barttfeld, P., Dottori, M., Gonzalez-Gomez, R., Migeot, J., Moguilner, S., Legaz, A., Hernandez, H., Prado, P., et al. (2025) · eBioMedicine · doi:10.1016/j.ebiom.2025.105614 · Cited by 14
Mindfulness Training for Young Neurosurgeons: A Virtual Multicenter Prospective Pilot Study
Pandit, A. S., Reka, A., Layard Horsfall, H., & Marcus, H. J. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.04.128 · Cited by 14
Prabhudesai, S., Wang, N. C., Ahluwalia, V., Huan, X., Bapuraj, J. R., Banovic, N., & Rao, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.740353 · Cited by 13
Azulai, S., Grinbaum, R., Beglaibter, N., Eldar, S. M., Rubin, M., Ben-Haroush Schyr, R., Romano-Zelekha, O., & Ben-Zvi, D. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10051144 · Cited by 13
Abdul-Khalek, N., Picciani, M., Shouman, O., Wimmer, R., Overgaard, M. T., Wilhelm, M., & Gregersen Echers, S. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00973 · Cited by 13
Noise dynamics of quantum annealers: estimating the effective noise using idle qubits
Pelofske, E., Hahn, G., & Djidjev, H. N. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/accbe6 · Cited by 12
Spinrad, A., Taylor, C. B., Ruzek, J. I., Jefroykin, S., Friedlander, T., Feleke, I., Lev-Ari, H., Szapiro, N., & Sadeh-Sharvit, S. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05570-0 · Cited by 12
Plant-associated fungi co-opt ancient antimicrobials for host manipulation
Mesny, F., Wolf, V., López-Moral, A., Kraege, A., Punt, W., Liu, S., Zhu, J., Park, J., Sato, Y., & Thomma, B. P. H. J. (2026) · Science Advances · doi:10.1126/sciadv.aec1406 · Cited by 11
Busch, F., Kaibel, L., Nguyen, H., Lemke, T., Ziegelmayer, S., Graf, M., Marka, A. W., Endrös, L., Prucker, P., Spitzl, D., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01483-w · Cited by 10
Universality of representation in biological and artificial neural networks
Hosseini, E., Casto, C., Zaslavsky, N., Conwell, C., Richardson, M., & Fedorenko, E. (2024) · doi:10.1101/2024.12.26.629294 · Cited by 10
The energetic cost of human walking as a function of uneven terrain amplitude
Hosseini-Yazdi, S.-S., & Kuo, A. D. (2025) · Journal of Experimental Biology · doi:10.1242/jeb.249840 · Cited by 10
Tilmon, S., Nyenhuis, S., Solomonides, A., Barbarioli, B., Bhargava, A., Birz, S., Bouzein, K., Cardenas, C., Carlson, B., Cohen, E., et al. (2023) · Journal of Clinical and Translational Science · doi:10.1017/cts.2023.670 · Cited by 10
Kalfarisi, R., Chew, A., Cai, J., Xue, M., Pok, J., & Wu, Z. Y. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103287 · Cited by 10
Molecular mechanisms of plastic biodegradation by the fungus Clonostachys rosea
Gambarini, V., Pavlov, N., Young, P., Dawes, S., Auffret, A., Kingsbury, J. M., Donaldson, L. A., Smith, D. A., Weaver, L., Pantos, O., et al. (2025) · mBio · doi:10.1128/mbio.00335-25 · Cited by 10
Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics
Thurimella, K., Mohamed, A. M. T., Graham, D. B., Owens, R. M., La Rosa, S. L., Plichta, D. R., Bacallado, S., & Xavier, R. J. (2023) · doi:10.1101/2023.10.23.563620 · Cited by 7
Papagiannakis, A., Yu, Q., Govers, S. K., Lin, W.-H., Wingreen, N. S., & Jacobs-Wagner, C. (2025) · doi:10.7554/elife.104276.1 · Cited by 4
AI-assisted tumor board decision-making in pancreatic oncology
Mergen, M., Busch, F., Schwarberg, B., Koldeweihe, P., Jungwirth, D., Sydlik, J., Maurer, H. C., Makowski, M. R., Spitzl, D., & Gassert, F. T. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03444-x · Cited by 3
A machine learning approach using autoencoders to perform quality control on meteorological data
Spohn, T. K., Walsh, E., Horan, K., O’Donoghue, J., Charnecki, T., Haslam, M., & Gallagher, S. (2026) · Environmental Data Science · doi:10.1017/eds.2026.10030 · Cited by 1
“A variational autoencoder assumes that latent variables follow a known prior distribution (usually Gaussian) and is limited by more complex training and tuning than a standard autoencoder.”
Retrieval Practice Versus Elaborative Encoding: A Systematic and Meta-analytic Review
Gonçalves, A. D. O., Muniz, B. F. B., & Jaeger, A. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10076-6 · Cited by 1
Quantifying the prevalence and impact of overreaching causal claims in social science
Isch, C., Dörr, T., Fasching, N., Jennings, G., & Watts, D. J. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02553-x
Metadata
- Authors
- Seabold, S., & Perktold, J.
- Venue
- Proceedings of the Python in Science Conference
- Publisher
- SciPy
- Published
- 2010
- Pages
- 92-96
- ISSN
- 2575-9752 (ISSN-L)
- OpenAlex
- W2181523240
- Cited by
- 6,465
- References
- 0
- FWCI
- 1.04
- Type
- conference paper
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
