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Welcome to PyQt-Fit’s documentation!

PyQt-Fit is a regression toolbox in Python with simple GUI and graphical tools to check your results. It currently handles regression based on user-defined functions with user-defined residuals (i.e. parametric regression) or non-parametric regression, either local-constant or local-linear, with the option to provide your own. The GUI currently provides an interface only to parametric regression.

Contents:

• Introduction to PyQt-Fit
• Regression using the GUI - tutorial
  • Using the interface
  • Defining your own function
  • Defining your own residual
• Parametric regression tutorial
  • Introduction
  • A simple example
  • Confidence Intervals
  • Defining the functions and residuals
  • Using the functions/residuals defined for the GUI
• Non-Parametric regression tutorial
  • Introduction
  • A simple example
  • Confidence Intervals
  • Types of Regressions
• Kernel Density Estimation tutorial
  • Introduction
  • A simple example
  • Boundary Conditions
  • Methods for Bandwidth Estimation
  • Transformations
• Modules of PyQt-Fit
  • Module pyqt_fit.plot_fit
  • Module pyqt_fit.curve_fitting
  • Module pyqt_fit.bootstrap
  • Module pyqt_fit.nonparam_regression
  • Module pyqt_fit.npr_methods
  • Module pyqt_fit.kde
  • Module pyqt_fit.kde_methods
  • Module pyqt_fit.kernels
  • Module pyqt_fit.utils

Indices and tables

• Index
• Module Index
• Search Page