Estimagic is a Python package that helps to build high-quality and user friendly implementations of (structural) econometric models.
It is designed with large structural models in mind. However, it is also useful for any other estimator that numerically minimizes or maximizes a criterion function (Extremum Estimator). Examples are maximum likelihood estimation, generalized method of moments, method of simulated moments and indirect inference.
Release files for estimagic 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| estimagic-0.3.0.tar.gz | 281.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| estimagic-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 598.0 kB
Release files / estimagic-0.3.0.tar.gz
| Download URL | estimagic-0.3.0.tar.gz |
|---|---|
| Size | 281.0 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.13
|
Release files / estimagic-0.3.0-py3-none-any.whl
| Download URL | estimagic-0.3.0-py3-none-any.whl |
|---|---|
| Size | 317.1 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.13
|
