optimagic - v0.5.2

Summary

This minor release adds support for two additional optimizer libraries:

• Nevergrad: A library for gradient-free optimization developed by Facebook Research.
• Bayesian Optimization: A library for constrained bayesian global optimization with Gaussian processes.

In addition, this release includes several bug fixes and improvements to the documentation. Many contributions in this release were made by Google Summer of Code (GSoC) 2025 applicants, with @gauravmanmode and @spline2hg being the accepted contributors.

Pull Requests

• #620 Uses interactive plotly figures in documentation (@timmens).
• #618 Improves bounds processing when no bounds are specified (@timmens).
• #615 Adds pre-commit hook that checks mypy version consistency (@timmens).
• #613 Exposes converter functionality (@spline2hg).
• #612 Fixes results processing to work with new cobyla optimizer (@janosg).
• #610 Adds needs_bounds and supports_infinite_bounds fields to algorithm info (@gauravmanmode).
• #608 Adds support for plotly >= 6 (@hmgaudecker, @timmens).
• #607 Returns run_explorations results in a dataclass (@r3kste).
• #605 Enhances batch evaluator checking and processing, introduces the internal BatchEvaluatorLiteral literal, and updates CHANGES.md (@janosg, @timmens).
• #602 Adds optimizer wrapper for bayesian-optimization package (@spline2hg).
• #601 Updates pre-commit hooks and fixes mypy issues (@janosg).
• #598 Fixes and adds links to GitHub in the documentation (@hamogu).
• #594 Refines newly added optimizer wrappers (@janosg).
• #591 Adds multiple optimizers from the nevergrad package (@gauravmanmode).
• #589 Rewrites the algorithm selection pre-commit hook in pure Python to address issues with bash scripts on Windows (@timmens).
• #586 and #592 Ensure the SciPy disp parameter is exposed for the following SciPy algorithms: slsqp, neldermead, powell, conjugategradient, newtoncg, cobyla, truncatednewton, trustconstr (@sefmef, @TimBerti).
• #585 Exposes all parameters of SciPy's BFGS optimizer in optimagic (@TimBerti).
• #582 Adds support for handling infinite gradients during optimization (@Aziz-Shameem).
• #579 Implements a wrapper for the PSO optimizer from the nevergrad package (@r3kste).
• #578 Integrates the intersphinx-registry package into the documentation for automatic linking to up-to-date external documentation (@Schefflera-Arboricola).
• #576 Wraps oneplusone optimizer from nevergrad (@gauravmanmode, @gulshan-123).
• #572 and #573 Fix bugs in error handling for parameter selector processing and constraints checking (@hmgaudecker).
• #570 Adds a how-to guide for adding algorithms to optimagic and improves internal documentation (@janosg).
• #569 Implements a threading batch evaluator (@spline2hg).
• #568 Introduces an initial wrapper for the migrad optimizer from the iminuit package (@spline2hg).
• #567 Makes the fun argument optional when fun_and_jac is provided (@gauravmanmode).
• #563 Fixes a bug in input harmonization for history plotting (@gauravmanmode).
• #552 Refactors and extends the History class, removing the internal HistoryArrays class (@timmens).
• #485 Adds bootstrap weights functionality (@alanlujan91).

- Python
Published by timmens 7 months ago

optimagic - v0.5.1

Summary

This is a minor release that introduces the new algorithm selection tool and several small improvements.

To learn more about the algorithm selection feature check out the following resources:

• How to specify and configure algorithms
• How to select local optimizers

Pull Requests

• #549 Add support for Python 3.13 (@timmens)
• #550 and #534 implement the new algorithm selection tool (@janosg)
• #548 and #531 improve the documentation (@ChristianZimpelmann)
• #544 Adjusts the results processing of the nag optimizers to be compatible with the latest releases (@timmens)
• #543 Adds support for numpy 2.x (@timmens)
• #536 Adds a how-to guide for choosing local optimizers (@mpetrosian)
• #535 Allows algorithm classes and instances in estimation functions (@timmens)
• #532 Makes several small improvements to the documentation (@janosg)

- Python
Published by timmens over 1 year ago

optimagic - v0.5.0

Summary

This is a major release with several breaking changes and deprecations. In this release we started implementing two major enhancement proposals and renamed the package from estimagic to optimagic (while keeping the estimagic namespace for the estimation capabilities).

• EP-02: Static typing
• EP-03: Alignment with SciPy

The implementation of the two enhancement proposals is not complete and will likely take until version 0.6.0. However, all breaking changes and deprecations (with the exception of a minor change in benchmarking) are already implemented such that updating to version 0.5.0 is future proof.

Pull Requests

• #500 removes the dashboard, the support for simopt optimizers and the derivative_plot (@janosg)
• #502 renames estimagic to optimagic (@janosg)
• #504 aligns maximize and minimize more closely with scipy. All related deprecations and breaking changes are listed below. As a result, scipy code that uses minimize with the arguments x0, fun, jac and method will run without changes in optimagic. Similarly, to OptimizeResult gets some aliases so it behaves more like SciPy's.
• #506 introduces the new Bounds object and deprecates lower_bounds, upper_bounds, soft_lower_bounds and soft_upper_bounds (@janosg)
• #507 updates the infrastructure so we can make parallel releases under the names optimagic and estimagic (@timmens)
• #508 introduces the new ScalingOptions object and deprecates the scaling_options argument of maximize and minimize (@timmens)
• #512 implements the new interface for objective functions and derivatives (@janosg)
• #513 implements the new optimagic.MultistartOptions object and deprecates the multistart_options argument of maximize and minimize (@timmens)
• #514 and #516 introduce the NumdiffResult object that is returned from first_derivative and second_derivative. It also fixes several bugs in the pytree handling in first_derivative and second_derivative and deprecates Richardson Extrapolation and the key (@timmens)
• #517 introduces the new NumdiffOptions object for configuring numerical differentiation during optimization or estimation (@timmens)
• #519 rewrites the logging code and introduces new LogOptions objects (@schroedk)
• #521 introduces the new internal algorithm interface. (@janosg and @mpetrosian)
• #522 introduces the new Constraint objects and deprecates passing dictionaries or lists of dictionaries as constraints (@timmens)

Breaking changes

• When providing a path for the argument logging of the functions maximize and minimize and the file already exists, the default behavior is to raise an error now. Replacement or extension of an existing file must be explicitly configured.
• The argument if_table_exists in log_options has no effect anymore and a corresponding warning is raised.
• OptimizeResult.history is now a optimagic.History object instead of a dictionary. Dictionary style access is implemented but deprecated. Other dictionary methods might not work.
• The result of first_derivative and second_derivative is now a optimagic.NumdiffResult object instead of a dictionary. Dictionary style access is implemented but other dictionary methods might not work.
• The dashboard is removed
• The derivative_plot is removed.
• Optimizers from Simopt are removed.
• Passing callables with the old internal algorithm interface as algorithm to minimize and maximize is not supported anymore. Use the new Algorithm objects instead. For examples see: https://tinyurl.com/24a5cner

Deprecations

• The criterion argument of maximize and minimize is renamed to fun (as in SciPy).
• The derivative argument of maximize and minimize is renamed to jac (as in SciPy)
• The criterion_and_derivative argument of maximize and minimize is renamed to fun_and_jac to align it with the other names.
• The criterion_kwargs argument of maximize and minimize is renamed to fun_kwargs to align it with the other names.
• The derivative_kwargs argument of maximize and minimize is renamed to jac_kwargs to align it with the other names.
• The criterion_and_derivative_kwargs argument of maximize and minimize is renamed to fun_and_jac_kwargs to align it with the other names.
• Algorithm specific convergence and stopping criteria are renamed to align them more with NlOpt and SciPy names.
  • convergence_relative_criterion_tolerance -> convergence_ftol_rel
  • convergence_absolute_criterion_tolerance -> convergence_ftol_abs
  • convergence_relative_params_tolerance -> convergence_xtol_rel
  • convergence_absolute_params_tolerance -> convergence_xtol_abs
  • convergence_relative_gradient_tolerance -> convergence_gtol_rel
  • convergence_absolute_gradient_tolerance -> convergence_gtol_abs
  • convergence_scaled_gradient_tolerance -> convergence_gtol_scaled
  • stopping_max_criterion_evaluations -> stopping_maxfun
  • stopping_max_iterations -> stopping_maxiter
• The arguments lower_bounds, upper_bounds, soft_lower_bounds and soft_upper_bounds are deprecated and replaced by optimagic.Bounds. This affects maximize, minimize, estimate_ml, estimate_msm, slice_plot and several other functions.
• The log_options argument of minimize and maximize is deprecated. Instead, LogOptions objects can be passed under the logging argument.
• The class OptimizeLogReader is deprecated and redirects to SQLiteLogReader.
• The scaling_options argument of maximize and minimize is deprecated. Instead a ScalingOptions object can be passed under the scaling argument that was previously just a bool.
• Objective functions that return a dictionary with the special keys "value", "contributions" and "rootcontributions" are deprecated. Instead, likelihood and least-squares functions are marked with a mark.likelihood or `mark.leastsquares decorator. There is a detailed how-to guide that shows the new behavior. This affects maximize,minimize,slice_plot` and other functions that work with objective functions.
• The multistart_options argument of minimize and maximize is deprecated. Instead, a MultistartOptions object can be passed under the multistart argument.
• Richardson Extrapolation is deprecated in first_derivative and second_derivative
• The key argument is deprecated in first_derivative and second_derivative
• Passing dictionaries or lists of dictionaries as constraints to maximize or minimize is deprecated. Use the new Constraint objects instead.

- Python
Published by timmens over 1 year ago

optimagic - v0.5.0rc2

Second release candidate for optimagic 0.5.0

This removes nlopt as mandatory pip dependency because the pip installation of nlopt is unstable.

- Python
Published by janosg over 1 year ago

optimagic - v0.5.0rc1

First release candidate for version 0.5.0

Summary

This is a major release with several breaking changes and deprecations. In this release we started implementing two major enhancement proposals and renamed the package from estimagic to optimagic (while keeping the estimagic namespace for the estimation capabilities).

• EP-02: Static typing
• EP-03: Alignment with SciPy

The implementation of the two enhancement proposals is not complete and will likely take until version 0.6.0. However, all breaking changes and deprecations (with the exception of a minor change in benchmarking) are already implemented such that updating to version 0.5.0 is future proof.

Pull Requests

• #500 removes the dashboard, the support for simopt optimizers and the derivative_plot (@janosg)
• #502 renames estimagic to optimagic (@janosg)
• #504 aligns maximize and minimize more closely with scipy. All related deprecations and breaking changes are listed below. As a result, scipy code that uses minimize with the arguments x0, fun, jac and method will run without changes in optimagic. Similarly, to OptimizeResult gets some aliases so it behaves more like SciPy's.
• #506 introduces the new Bounds object and deprecates lower_bounds, upper_bounds, soft_lower_bounds and soft_upper_bounds (@janosg)
• #507 updates the infrastructure so we can make parallel releases under the names optimagic and estimagic (@timmens)
• #508 introduces the new ScalingOptions object and deprecates the scaling_options argument of maximize and minimize (@timmens)
• #512 implements the new interface for objective functions and derivatives (@janosg)
• #513 implements the new optimagic.MultistartOptions object and deprecates the multistart_options argument of maximize and minimize (@timmens)
• #514 and #516 introduce the NumdiffResult object that is returned from first_derivative and second_derivative. It also fixes several bugs in the pytree handling in first_derivative and second_derivative and deprecates Richardson Extrapolation and the key (@timmens)
• #517 introduces the new NumdiffOptions object for configuring numerical differentiation during optimization or estimation (@timmens)
• #519 rewrites the logging code and introduces new LogOptions objects ({ghuser}schroedk)
• #521 introduces the new internal algorithm interface. (@janosg and @mpetrosian)
• #522 introduces the new Constraint objects and deprecates passing dictionaries or lists of dictionaries as constraints (@timmens)

Breaking changes

• When providing a path for the argument logging of the functions maximize and minimize and the file already exists, the default behavior is to raise an error now. Replacement or extension of an existing file must be explicitly configured.
• The argument if_table_exists in log_options has no effect anymore and a corresponding warning is raised.
• OptimizeResult.history is now a optimagic.History object instead of a dictionary. Dictionary style access is implemented but deprecated. Other dictionary methods might not work.
• The result of first_derivative and second_derivative is now a optimagic.NumdiffResult object instead of a dictionary. Dictionary style access is implemented but other dictionary methods might not work.
• The dashboard is removed
• The derivative_plot is removed.
• Optimizers from Simopt are removed.
• Passing callables with the old internal algorithm interface as algorithm to minimize and maximize is not supported anymore. Use the new Algorithm objects instead. For examples see: https://tinyurl.com/24a5cner

Deprecations

• The criterion argument of maximize and minimize is renamed to fun (as in SciPy).
• The derivative argument of maximize and minimize is renamed to jac (as in SciPy)
• The criterion_and_derivative argument of maximize and minimize is renamed to fun_and_jac to align it with the other names.
• The criterion_kwargs argument of maximize and minimize is renamed to fun_kwargs to align it with the other names.
• The derivative_kwargs argument of maximize and minimize is renamed to jac_kwargs to align it with the other names.
• The criterion_and_derivative_kwargs argument of maximize and minimize is renamed to fun_and_jac_kwargs to align it with the other names.
• Algorithm specific convergence and stopping criteria are renamed to align them more with NlOpt and SciPy names.
  • convergence_relative_criterion_tolerance -> convergence_ftol_rel
  • convergence_absolute_criterion_tolerance -> convergence_ftol_abs
  • convergence_relative_params_tolerance -> convergence_xtol_rel
  • convergence_absolute_params_tolerance -> convergence_xtol_abs
  • convergence_relative_gradient_tolerance -> convergence_gtol_rel
  • convergence_absolute_gradient_tolerance -> convergence_gtol_abs
  • convergence_scaled_gradient_tolerance -> convergence_gtol_scaled
  • stopping_max_criterion_evaluations -> stopping_maxfun
  • stopping_max_iterations -> stopping_maxiter
• The arguments lower_bounds, upper_bounds, soft_lower_bounds and soft_upper_bounds are deprecated and replaced by optimagic.Bounds. This affects maximize, minimize, estimate_ml, estimate_msm, slice_plot and several other functions.
• The log_options argument of minimize and maximize is deprecated. Instead, LogOptions objects can be passed under the logging argument.
• The class OptimizeLogReader is deprecated and redirects to SQLiteLogReader.
• The scaling_options argument of maximize and minimize is deprecated. Instead a ScalingOptions object can be passed under the scaling argument that was previously just a bool.
• Objective functions that return a dictionary with the special keys "value", "contributions" and "rootcontributions" are deprecated. Instead, likelihood and least-squares functions are marked with a mark.likelihood or `mark.leastsquares decorator. There is a detailed how-to guide that shows the new behavior. This affects maximize,minimize,slice_plot` and other functions that work with objective functions.
• The multistart_options argument of minimize and maximize is deprecated. Instead, a MultistartOptions object can be passed under the multistart argument.
• Richardson Extrapolation is deprecated in first_derivative and second_derivative
• The key argument is deprecated in first_derivative and second_derivative
• Passing dictionaries or lists of dictionaries as constraints to maximize or minimize is deprecated. Use the new Constraint objects instead.

- Python
Published by janosg over 1 year ago

optimagic - v0.4.7

v0.4.7

This release contains minor improvements and bug fixes. It is the last release before the package will be renamed to optimagic and two large enhancement proposals will be implemented.

• #490 adds the attribute optimize_result to the MomentsResult class (@timmens)
• #483 fixes a bug in the handling of keyword arguments in bootstrap (@alanlujan91)
• #477 allows to use an identity weighting matrix in MSM estimation (@sidd3888)
• #473 fixes a bug where bootstrap keyword arguments were ignored get_moments_cov (@timmens)
• #467, #478, #479 and #480 improve the documentation (@mpetrosian, @segsell, and @timmens)

- Python
Published by janosg over 1 year ago

optimagic - v0.4.6

This release drastically improves the optimizer benchmarking capabilities, especially with noisy functions and parallel optimizers. It makes tranquilo and numba optional dependencies and is the first version of estimagic to be compatible with Python 3.11.

• #464 Makes tranquilo and numba optional dependencies (@janosg)
• #461 Updates docstrings for procssbenchmarkresults (@segsell)
• #460 Fixes several bugs in the processing of benchmark results with noisy functions (@janosg)
• #459 Prepares benchmarking functionality for parallel optimizers (@mpetrosian and @janosg)
• #457 Removes some unused files (@segsell)
• #455 Improves a local pre-commit hook (@ChristianZimpelmann)

- Python
Published by janosg over 2 years ago

optimagic - v0.4.5

• #379 Improves the estimation table (@ChristianZimpelmann)
• #445 fixes line endings in local pre-commit hook (@ChristianZimpelmann)
• #443, #444, #445, #446, #448 and #449 are a major refactoring of tranquilo (@timmens and @janosg)
• #441 Adds an aggregated convergence plot for benchmarks (@mpetrosian)
• #435 Completes the cartis-roberts benchmark set (@segsell)

- Python
Published by janosg almost 3 years ago

optimagic - v0.4.4

• #437 removes fuzzywuzzy as dependency (@aidatak97)
• #432 makes logging compatible with sqlalchemy 2.x (@janosg)
• #430 refactors the getter functions in Tranquilo (@janosg)
• #427 improves pre-commit setup (@timmens and @hmgaudecker)
• #425 improves handling of notebooks in documentation (@baharcos)
• #423 and #399 add code to calculate poisdeness constants (@segsell)
• #420 improve CI infrastructure (@hmgaudecker, @janosg)
• #407 adds global optimizers from scipy (@baharcos)

- Python
Published by janosg about 3 years ago

optimagic - v0.4.3

This is a minor release that mainly improves the installation

• #416 pins a minimum scipy version and adds numba as pip dependency. It also adds bounds support for scipy neldermead (@janosg)

- Python
Published by janosg about 3 years ago

optimagic - v0.4.2

This realease contains a bugfix and several improvements

If you have used multistart optimizations with a least squares optimizer you should update as quickly as possible.

412 Improves the output of the fides optimizer among other small changes (@janosg)

411 Fixes a bug in multistart optimizations with least squares optimizers. See #410 for details (@janosg)

404 speeds up the gqtpar subsolver (@mpetrosian )

400 refactors subsolvers (@mpetrosian)

398, #397, #395, #390, #389, #388 continue with the implementation of tranquilo (@segsell, @timmens, @mpetrosian, @janosg)

391 speeds up the bntr subsolver

- Python
Published by janosg over 3 years ago

optimagic - v0.4.1

This is a minor polishing release.

• #307 Adopts a code of condact and governance model
• #384 384 Polish documentation @janosg and @mpetrosian
• #374 Moves the documentation to MyST @baharcos
• #365 Adds copybuttos to documentation @amageh
• #371 Refactors the pounders algorithm @segsell
• #369 Fixes CI @janosg
• #367 Fixes the linux environment @timmens
• #294 Adds the very first experimental version of tranquilo @janosg, @timmens, @segsell, @mpetrosian

- Python
Published by janosg over 3 years ago

optimagic - v0.4.0

• #366 prepares changes log for next release (@segsell)
• #362 polishes documentation and adds copy button for code-snippets (@segsell)

- Python
Published by segsell over 3 years ago

optimagic - v0.3.4

• #361 fixes multiple bugs in estimate_msm and estimate_ml that only happened in constrained estimations (@timmens, @janosg, @segsell)
• #347 adds a BootstrapResult object similar to other estimation results (@segsell)

- Python
Published by janosg over 3 years ago

optimagic - v0.3.3

First release with full jax support (i.e. jax arrays do not have to be hidden from estimagic)

• #357 Adds jax support @janosg
• #359 Improves error handling with violated constaints @timmens
• #358 Improves cartis roberts set of test functions and improves the default latex rendering of MultiIndex tables @mpetrosian

- Python
Published by janosg over 3 years ago

optimagic - v0.3.2

Polishing and multiple small bugfixes.

- Python
Published by janosg over 3 years ago

optimagic - v0.3.1

This is the first version of estimagic with experimental support for nonlinear constraints. Moreover, we fix some bugs and improve test coverage.

• #349 fixes bugs introduced in the transition to pytrees and custom results objects. Details are described in the PR comments.
• #346 Adds experimental support for nonlinear constraints

- Python
Published by janosg over 3 years ago

optimagic - v0.3.0

Summary

Fist release with pytree support in optimization, estimation and differentiation and much better result objects in optimization and estimation.

Breaking changes

• New OptimizeResult object is returned by maximize and minimize. This breaks all code that expects the old result dictionary. Usage of the new result is explained in the getting started tutorial on optimization.
• New internal optimizer interface that can break optimization with custom optimizers
• The inferface of process_constraints changed quite drastically. This breaks code that used process_constraints to get the number of free parameters or check if constraints are valid. There are new high level functions estimagic.check_constraints and estimagic.count_free_params instead.
• Some functions from estimagic.logging.read_log are removed and replaced by estimagic.OptimizeLogReader.
• Convenience functions to create namedtuples are removed from estimagic.utilities.

PRs

• #345 Moves estimation_table to new latex functionality of pandas (mpetrosian)
• #344 Adds pytree support to slice_plot (janosg)
• #343 Improves the result object of estimation functions and makes msm estimation pytree compatible (janosg)
• #342 Improves default options of the fides optimizer, allows single constraints and polishes the documentation (janosg)
• #340 Enables history collection for optimizers that evaluate the criterion function in parallel (janosg)
• #339 Incorporates user feedback and polishes the documentation (janosg)
• #338 Improves log reading functions (janosg)
• #336 Adds pytree support to the dashboard (roecla).
• #335 Introduces an OptimizeResult object and functionality for history plotting (janosg).
• #333 Uses new history collection feature to speed up benchmarking (segsell).
• #330 Is a major rewrite of the estimation code (timmens).
• #328 Improves quadratic surrogate solvers used in pounders and tranquilo (segsell).
• #326 Improves documentation of numerical derivatives (timmens).
• #325 Improves the slice_plot (mpetrosian)
• #324 Adds ability to collect optimization histories without logging (janosg).
• #311 and #288 rewrite all plotting code in plotly (timmens and aidatak97).
• #306 improves quadratic surrogate solvers used in pounders and tranquilo (segsell).
• #305 allows pytrees during optimization and rewrites large parts of the constraints processing (janosg).
• #303 introduces a new optimizer interface that makes it easier to add optimizers and makes it possible to access optimizer specific information outside of the intrenalcriterionand_derivative (janosg and roecla).

- Python
Published by janosg over 3 years ago

optimagic - v0.2.4

Remove chaospy as dependency

- Python
Published by janosg almost 4 years ago

optimagic - v0.2.3

• Pytree support for first and second derivative
• Add fast solvers for quadratic trustregion subproblems
• Improved estimation tables
• Various small bugfixes

- Python
Published by janosg almost 4 years ago

optimagic - v0.2.2

First release with own optimization algorithms. - parallel Nelder-Mead by Jacek Barszczewski - parallel Pounders by Sebastian Gsell

The algorithms are still work in progress and will get more convergence criteria, support for bounds, etc. in the future.

- Python
Published by janosg about 4 years ago

optimagic - v0.2.1

Mainly improvements to the documentation.

- Python
Published by roecla about 4 years ago

optimagic - v0.2.0

Release 0.2.0

Add a lot of new functionality with a few minor breaking changes. We have more optimizers, better error handling, bootstrap and inference for method of simulated moments.

Breaking changes

• logging is disabled by default during optimization.
• the logoption "ifexists" was renamed to "iftableexists"
• The comparison plot function is removed.
• first_derivative now returns a dictionary, independent of arguments.
• structure of the logging database has changed
• there is an additional boolean flag named scaling in minimize and maximize

New features

• Optimizer benchmarking
• Multistart optimization
• More optimizers (fides, ipopt, nlopt, pygmo)
• estimate_msm and estimate_ml functions
• More diagnostic tools for numerical derivatives

- Python
Published by janosg about 4 years ago

optimagic - v0.1.3

• Improve numerical stability of inference functions
• Better documentation

- Python
Published by janosg over 4 years ago

optimagic - v0.1.2

• Small fixes to documentation
• Now compatible with newest pandas
• By default not all inputs to maximize/minimize are saved in database

- Python
Published by janosg about 5 years ago

optimagic - v0.1.1

Complete overhaul of estimagic.

• Restructured documentation
• New internal optimizer interface
• Better handling of derivatives
• Harmonized algo_options

- Python
Published by janosg about 5 years ago

optimagic - v0.0.31

- Python
Published by tobiasraabe over 5 years ago

optimagic - v0.0.30

• Allow custom gradient in maximize and minimize

- Python
Published by janosg almost 6 years ago

optimagic - v0.0.29

• Make estimagic compatible with pandas 1.0

- Python
Published by janosg almost 6 years ago

optimagic - v0.0.28

• New command line interface to dashboard
• Estimagic becomes a noarch package

- Python
Published by janosg almost 6 years ago