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estimagic
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  • Getting Started
    • Installation
    • Numerical optimization
    • Estimation with estimagic
      • Likelihood estimation
      • Method of Simulated Moments (MSM)
    • Numerical differentiation
  • How-to Guides
    • Optimization
      • estimagic tutorial at SciPy2022 conference
      • How to specify the criterion function and its derivatives
      • How to specify params
      • How to specify algorithms and algorithm specific options
      • How to specify bounds
      • How to specify constraints
      • How to use logging
      • How to use the dashboard
      • How to handle errors during optimization
      • How to scale optimization problems
      • How to do multistart optimizations
      • How to Benchmark Optimization Algorithms
      • How to visualize optimizer histories
      • How to visualize an optimization problem
      • Which optimizer to use
    • Differentiation
      • How to calculate first derivatives
      • How to calculate second derivatives
      • How to plot derivatives
    • Inference
      • How to calculate standard errors for likelihood models
      • How to calculate standard errors for method of simulated moments
      • Bootstrap Tutorial
    • Miscellaneous Topics
      • How to generate publication quality tables
      • How to use batch evaluators
      • How to visualize and interpret sensitivity measures
      • Frequently Asked Questions
  • Explanations
    • Optimization
      • How constraints are implemented
      • Internal optimizers for estimagic
      • Why optimization is difficult
      • Introduction to basic types of numerical optimization algorithms
      • How supported optimization algorithms are tested
    • Differentiation
      • Background and methods
      • Richardson Extrapolation
    • Inference
      • Bootstrap Confidence Intervals
      • Bootstrap Monte Carlo Comparison
      • Robust Likelihood inference
  • API
    • Utility functions
    • The default algorithm options
    • Batch evaluators
  • Development
    • Code of Conduct
    • How to contribute
    • Styleguide
    • EEPs
      • EEP-00: Governance model & code of conduct
      • EEP-01: Pytrees
      • EEP-02: Static typing
      • EEP-03: Alignment with SciPy
    • Credits
    • Changes
  • Videos
  • Optimizers
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Python Module Index

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- estimagic
    estimagic.batch_evaluators
    estimagic.optimization.algo_options
    estimagic.utilities
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