Welcome to the calibr8 documentation!

With calibr8 we take a fresh perspective on calibration modeling.

You can download the latest version from GitHub or install it via pip install calibr8.

In the following chapters, we introduce the terminology, basic concepts and most important features.

Contents

• 1. Terminology
• 2. How to estimate calibration model parameters
• 3. Applying calibration models: Useful features
  • 3.1. Saving & Loading
  • 3.2. Numerical Inference

Learn from the examples how a calibr8-driven workflow looks like:

Examples

• The Exponential Model
• The Generalized Logistic Function
  • Example
  • Summary
• Fitting a Linear Model
  • Inspecting the calibration data points
  • Setting up the model
  • Fitting the model to data
  • Using the fitted model
• Numeric posterior of the independent variable
  • Synthetic groundtruth
  • Model definition
  • Model fit
  • Visualization
  • Plot for one observation, where predict_independent is similar to the median
  • Comparison with MCMC for two observations
• In-depth Model Fitting
  • Generating fake data
  • Setting up the model
  • Fitting
• Counting cells with a Poisson noise model
  • Implementing a noise model
  • Using the noise model in a custom calibration model
  • Visualizing the fitted model
  • Applying the model for UQ
  • Conclusion
• Bayesian hierarchical calibration models
  • Create the models
  • Simulate a dataset
  • Building the Bayesian model
  • Visualization of the results
  • Conclusion

You can find autogenerated API documentation below:

API Reference

• Inheritance and composition of calibr8 models
  • Inheritance of model classes
  • Composition of distribution mixins
  • Localization of implementations
• calibr8.core
  • CalibrationModel
  • ContinuousInference
  • ContinuousMultivariateInference
  • ContinuousMultivariateModel
  • ContinuousUnivariateInference
  • ContinuousUnivariateModel
  • DistributionMixin
  • InferenceResult
  • asymmetric_logistic()
  • exponential()
  • infer_independent()
  • inverse_asymmetric_logistic()
  • inverse_exponential()
  • inverse_log_log_logistic()
  • inverse_logistic()
  • inverse_xlog_asymmetric_logistic()
  • inverse_xlog_logistic()
  • inverse_ylog_logistic()
  • log_log_logistic()
  • logistic()
  • polynomial()
  • xlog_asymmetric_logistic()
  • xlog_logistic()
  • ylog_logistic()
• calibr8.contrib.normal
  • BaseAsymmetricLogisticN
  • BaseExponentialModelN
  • BaseLogIndependentAsymmetricLogisticN
  • BasePolynomialModelN
• calibr8.contrib.studentt
  • BaseAsymmetricLogisticT
  • BaseExponentialModelT
  • BaseLogIndependentAsymmetricLogisticT
  • BaseModelT
  • BasePolynomialModelT
• calibr8.optimization
  • fit_scipy()
  • fit_scipy_global()
• calibr8.utils
  • BuildMismatchException
  • CompatibilityException
  • ImportWarner
  • MajorMismatchException
  • MinorMismatchException
  • PatchMismatchException
  • assert_version_match()
  • check_scale_degree()
  • format_datetime()
  • istensor()
  • parse_datetime()
  • plot_continuous_band()
  • plot_model()
  • plot_norm_band()
  • plot_t_band()