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This design ensures that users benefit from the performance and reliability\r\nof the original Fortran implementation, while working within the modern Python ecosystem.  \r\n\r\nODR, also known as [errors-in-variables regression], is designed primarily for instances when both\r\nthe explanatory and response variables have significant errors.\r\n\r\n\u003cp align=\"center\"\u003e\r\n  \u003cimg src=\"https://github.com/HugoMVale/odrpack-python/blob/main/docs/odr.png\" width=\"250\" alt=\"Deming regression; special case of ODR.\" style=\"margin-right: 10px;\"\u003e\r\n\u003c/p\u003e\r\n\r\n[errors-in-variables regression]: https://en.wikipedia.org/wiki/Errors-in-variables_models\r\n[odrpack95]: https://github.com/HugoMVale/odrpack95\r\n\r\n\r\n## Installation\r\n\r\nYou can install the package via pip:\r\n\r\n```sh\r\npip install odrpack\r\n```\r\n\r\n## Documentation and Usage\r\n\r\nThe following example demonstrates a simple use of the package. For more comprehensive examples and explanations, please refer to the [documentation](https://hugomvale.github.io/odrpack-python/) pages.\r\n\r\n```py\r\nfrom odrpack import odr_fit\r\nimport numpy as np\r\n\r\nxdata = np.array([0.982, 1.998, 4.978, 6.01])\r\nydata = np.array([2.7, 7.4, 148.0, 403.0])\r\n\r\nbeta0 = np.array([2.0, 0.5])\r\nbounds = (np.array([0.0, 0.0]), np.array([10.0, 0.9]))\r\n\r\ndef f(x: np.ndarray, beta: np.ndarray) -\u003e np.ndarray:\r\n    \"Model function.\"\r\n    return beta[0] * np.exp(beta[1]*x)\r\n\r\nsol = odr_fit(f, xdata, ydata, beta0, bounds=bounds)\r\n\r\nprint(\"beta:\", sol.beta)\r\nprint(\"delta:\", sol.delta)\r\n```\r\n\r\n```sh\r\nbeta: [1.63336897 0.9       ]\r\ndelta: [-0.36885696 -0.31272648  0.02929022  0.11031872]\r\n```\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhugomvale%2Fodrpack-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhugomvale%2Fodrpack-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhugomvale%2Fodrpack-python/lists"}