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LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python
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Identification of strontium in the merger of two neutron stars
Watson, D., Hansen, C. J., Selsing, J., Koch, A., Malesani, D. B., Andersen, A. C., Fynbo, J. P. U., Arcones, A., Bauswein, A., Covino, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1676-3 · Cited by 482
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Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex
Seeman, S. C., Campagnola, L., Davoudian, P. A., Hoggarth, A., Hage, T. A., Bosma-Moody, A., Baker, C. A., Lee, J. H., Mihalas, S., Teeter, C., et al. (2018) · eLife · doi:10.7554/elife.37349 · Cited by 183
Metadata
- Authors
- Newville, M., Otten, R., Nelson, A., Stensitzki, T., Ingargiola, A., Allan, D., Fox, A., Carter, F., Michał, Osborn, R., Pustakhod, D., Weigand, S., lneuhaus, Aristov, A. I., Glenn, Mark, mgunyho, Deil, C., Hansen, A. L. R., Pasquevich, G., Foks, N. L., Zobrist, N., Frost, O., Stuermer, Jaskula, J.-C., Caldwell, S., Eendebak, P. T., Pompili, M., Nielsen, J. H., & Persaud, A.
- Venue
- Zenodo (CERN European Organization for Nuclear Research)
- Publisher
- European Organization for Nuclear Research
- Published
- 2014-09-21
- OpenAlex
- W6912592130
- Cited by
- 1,680
- References
- 0
- Type
- software
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python. https://app.cochat.ai/papers/10.5281/zenodo.598352
- The paper
- Newville, M., Otten, R., Nelson, A., Stensitzki, T., Ingargiola, A., Allan, D., Fox, A., Carter, F., Michał, Osborn, R., Pustakhod, D., Weigand, S., lneuhaus, Aristov, A. I., Glenn, Mark, mgunyho, Deil, C., Hansen, A. L. R., . . . Persaud, A. (2014). LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.598352
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.