clophfit.fitting.odr
====================

.. py:module:: clophfit.fitting.odr

.. autoapi-nested-parse::

   Orthogonal Distance Regression (ODR) utilities and fitting pipeline.



Functions
---------

.. autoapisummary::

   clophfit.fitting.odr.format_estimate
   clophfit.fitting.odr.generalized_combined_model
   clophfit.fitting.odr.fit_binding_odr
   clophfit.fitting.odr.outlier


Module Contents
---------------

.. py:function:: format_estimate(value, error, significant_digit_limit = 5)

   Format the value and its associated error into "{value} ± {error}" string.


.. py:function:: generalized_combined_model(pars, x, dataset_lengths, *, is_ph)

   Handle multiple datasets with different lengths and masks.


.. py:function:: fit_binding_odr(ds_or_fr, *, reweight = False, remove_outliers = None, max_iter = 15, tol = 0.1)

   Analyze multi-label titration datasets using ODR.

   :param ds_or_fr: Either a Dataset (will run initial LS fit) or a FitResult with initial params.
   :type ds_or_fr: Dataset | FitResult[MiniT]
   :param reweight: Whether to perform iterative reweighting (recursive ODR).
   :type reweight: bool
   :param remove_outliers: Outlier removal configuration, e.g., "zscore:2.5" or "zscore:2.5:5".
                           If set, performs recursive ODR and masks outliers iteratively.
   :type remove_outliers: str | None
   :param max_iter: Maximum number of iterations for recursive ODR.
   :type max_iter: int
   :param tol: Convergence tolerance for residual variance.
   :type tol: float

   :returns: ODR fitting results. Residuals are WEIGHTED by the ORIGINAL y_err
             (before ODR modified it), making them comparable to LM residuals.
   :rtype: FitResult[odrpack.OdrResult]


.. py:function:: outlier(output, *, threshold = 2.0, plot_z_scores = False)

   Identify outliers.