Package index
Main Entry Points
The two functions most users call directly. Both expect preprocessed data on the fitting scale (use curveRcore::preprocess_standards() upstream).
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bead_assay_example - Synthetic Multi-Plate Bead Assay Example Dataset
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elisa_assay_example - Synthetic Multi-Plate ELISA Example Dataset
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fit_calibration_freq() - Fit a Frequentist Calibration Curve (Single Curve)
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fit_calibration_freq_multiplate() - Fit Frequentist Calibration Curves Across Multiple Curves
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collect_samples() - Extract All Sample Predictions from Multi-Curve Results
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summary_table() - Extract a Combined Summary Table from Multi-Curve Results
NLS Fitting Engine
Lower-level functions that implement multi-start Levenberg-Marquardt fitting for one plate. Called internally by fit_calibration_freq() but exported for users who need fine-grained control.
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compute_model_constraints() - Compute Parameter Bounds for All Candidate Models
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fit_ensemble_nls() - Fit Ensemble of NLS Models for One Plate
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generate_start_lists() - Generate Multi-Start Lists for NLS Fitting
Model Selection
AIC-based ranking and best-parameter extraction. select_best_aic() is a pure ranking step; eligibility gating is handled upstream by curveRcore::select_best_eligible().
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summarise_ensemble() - Summarise Ensemble Fit Statistics
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extract_best_parameters() - Extract Parameters from the Best Fit
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select_best_aic() - Select the Best Model by AIC
Grid Prediction & Uncertainty
Evaluates the best-fit model across the prediction grid, computes delta-method confidence intervals on the response, back-calculates concentration, and propagates uncertainty to se_concentration and pcov.
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predict_grid_freq() - Predict Grid with Uncertainty (Frequentist)
Sample Back-Calculation
Applies the analytical model inverse to test-sample responses, propagates uncertainty via the delta method, and multiplies by the dilution factor to produce final_concentration.
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predict_samples_freq() - Predict Concentration for Test Samples