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MacroStat 0.6.0 documentation - Home MacroStat 0.6.0 documentation - Home
  • User Guide
  • API Reference
  • Model Library
  • Changelog
  • Developing MacroStat
    • Project Information
    • References
  • GitHub
  • User Guide
  • API Reference
  • Model Library
  • Changelog
  • Developing MacroStat
  • Project Information
  • References
  • GitHub

Section Navigation

  • Getting Started
  • Core Model Structure
    • Model
    • Parameters
    • Scenarios
    • Variables
    • Behavior
    • Constraints
  • Differentiability and Jacobians
  • Parameter constraints example
  • Jacobian diagnostics
  • Parameter Estimation and Calibration
  • Causality Analysis
    • Causality Base Class
    • Docstring Causality
  • Parameter Space Sampling
    • Base Sampler Class
    • Sobol Sampler
  • LaTeX Interfaces
  • User Guide

User Guide#

This section contains some useful guides to using MacroStat. For a comprehensive guide to the implemented models, see Model Library, and for the full API, see API reference.

  • Getting Started
  • Core Model Structure
    • Introduction
    • Module Components
  • Differentiability and Jacobians
    • Overview
    • Quick start
    • Direct vs log-space perturbations
    • High-level differentiability check
    • Per-parameter Jacobian comparison
    • Expected disagreements
    • Test model: LINEAR2D
  • Parameter constraints example
    • A minimal Parameters subclass
    • What happens when defaults violate the constraint?
    • The happy path: free parameters move, derived parameter follows
    • Autograd consequence: derived parameter has zero gradient
    • Real-world example: GL06INSOUT
    • Closing notes
  • Jacobian diagnostics
    • LINEAR2D: direct-space differences
    • GL06SIMEX: log-space on a smooth SFC model
    • GL06INSOUT: localising a step-function disagreement
    • Testing different values of epsilon in the numerical differentiation
    • Decision tree: reading a compare_jacobian_dicts report
  • Parameter Estimation and Calibration
    • Overview
    • Quick start
    • Loss functions
    • Calibration best practices
    • Working with EstimationResult
    • Integration with differentiability tools
    • Levenberg-Marquardt optimization
    • Complete calibration examples
    • See also
  • Causality Analysis
    • The Causality Module
    • Docstring Causality
  • Parameter Space Sampling
    • The Sample Module
  • LaTeX Interfaces

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