Name | rustmfx JSON |
Version |
0.1.1
JSON |
| download |
home_page | None |
Summary | A Rust-based Statistics and ML package, callable from Python. |
upload_time | 2025-02-16 17:49:34 |
maintainer | None |
docs_url | None |
author | None |
requires_python | >=3.8 |
license | MIT |
keywords |
rust
python
machine learning
statistics
pyo3
|
VCS |
 |
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
|
# FeBOLT-
[](https://github.com/luke-brosnan-cbc/FeBOLT/actions)
[](https://pypi.org/project/febolt/)
## Introduction
As datasets continue to grow in size, economists, social scientists, and data analysts require more efficient tools for statistical modeling and inference. Traditional Python libraries like `statsmodels` provide robust inference capabilities but can be slow and memory-intensive, making them impractical for large datasets. Meanwhile, `scikit-learn` offers efficient machine learning tools but lacks the depth of statistical inference needed for rigorous empirical research.
Enter `Febolt`: a high-performance statistical modeling package built with Rust to provide **fast, memory-efficient**, and **fully-featured inference** capabilities. `FeBOLT` is designed to bridge the gap between performance and analytical depth, making it an ideal choice for researchers working with large-scale data.
## Features
- **Probit, Logit, and OLS Models**: Supports fundamental regression models with additional enhancements.
- **Weighted Regression**: Apply observation weights to models.
- **Clustered and Robust Standard Errors**: More reliable inference with robust and cluster-adjusted SEs.
- **Average Marginal Effects (AMEs)**: Compute AMEs for Logit and Probit models.
- **Rust-Powered Performance**: Significantly faster computations compared to Python-based alternatives.
- **Optimized for 32-bit and 64-bit Floats**: Choose between improved memory efficiency with 32-bit floats or higher precision with 64-bit floats.
## Why FeBOLT?
### **Performance Meets Inference**
Unlike `scikit-learn`, which focuses on machine learning without comprehensive inference support, `FeBOLT` is built specifically for statistical modeling while maintaining **speed and efficiency**. Unlike `statsmodels`, which can be bulky and slow for large datasets, `FeBOLT` leverages **Rust’s performance optimizations** to provide rapid computations without sacrificing analytical power.
### **Memory Efficiency for Large Datasets**
Economists and social scientists often deal with panel datasets and large-scale survey data, where traditional inference models become infeasible due to memory constraints. `FeBOLT` allows the use of **32-bit floats** to **significantly reduce memory usage**, while still offering **64-bit float precision** for cases where accuracy is paramount.
### **Inference Without Compromise**
While `scikit-learn` lacks built-in inference tools like **robust and clustered standard errors**, `FeBOLT` incorporates these essential statistical features to support rigorous empirical research. Whether you need **fast OLS regression** or **efficient Probit/Logit estimation with AMEs**, `FeBOLT` delivers both speed and accuracy in one package.
## Installation
```bash
pip install febolt
```
## Quick Start
```python
import febolt
# Example usage (to be filled in)
```
## Performance
`FeBOLT` outperforms `statsmodels` and `scikit-learn` by leveraging Rust’s speed and memory efficiency. This results in significantly faster execution times, especially for large datasets and models requiring robust standard errors.
## Contributing
Contributions are welcome! Feel free to submit issues and pull requests on [GitHub](https://github.com/luke-brosnan-cbc/FeBOLT).
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Raw data
{
"_id": null,
"home_page": null,
"name": "rustmfx",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "rust, python, Machine Learning, Statistics, pyo3",
"author": null,
"author_email": "Luke Brosnan <luke.brosnan.cbc@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/ad/be/26eeefc9eaf4bace611486f65b601c801b513a54bff807da6926531bdd47/rustmfx-0.1.1.tar.gz",
"platform": null,
"description": "# FeBOLT-\n\n[](https://github.com/luke-brosnan-cbc/FeBOLT/actions)\n[](https://pypi.org/project/febolt/)\n\n## Introduction\n\nAs datasets continue to grow in size, economists, social scientists, and data analysts require more efficient tools for statistical modeling and inference. Traditional Python libraries like `statsmodels` provide robust inference capabilities but can be slow and memory-intensive, making them impractical for large datasets. Meanwhile, `scikit-learn` offers efficient machine learning tools but lacks the depth of statistical inference needed for rigorous empirical research.\n\nEnter `Febolt`: a high-performance statistical modeling package built with Rust to provide **fast, memory-efficient**, and **fully-featured inference** capabilities. `FeBOLT` is designed to bridge the gap between performance and analytical depth, making it an ideal choice for researchers working with large-scale data.\n\n## Features\n\n- **Probit, Logit, and OLS Models**: Supports fundamental regression models with additional enhancements.\n- **Weighted Regression**: Apply observation weights to models.\n- **Clustered and Robust Standard Errors**: More reliable inference with robust and cluster-adjusted SEs.\n- **Average Marginal Effects (AMEs)**: Compute AMEs for Logit and Probit models.\n- **Rust-Powered Performance**: Significantly faster computations compared to Python-based alternatives.\n- **Optimized for 32-bit and 64-bit Floats**: Choose between improved memory efficiency with 32-bit floats or higher precision with 64-bit floats.\n\n## Why FeBOLT?\n\n### **Performance Meets Inference**\nUnlike `scikit-learn`, which focuses on machine learning without comprehensive inference support, `FeBOLT` is built specifically for statistical modeling while maintaining **speed and efficiency**. Unlike `statsmodels`, which can be bulky and slow for large datasets, `FeBOLT` leverages **Rust\u2019s performance optimizations** to provide rapid computations without sacrificing analytical power.\n\n### **Memory Efficiency for Large Datasets**\nEconomists and social scientists often deal with panel datasets and large-scale survey data, where traditional inference models become infeasible due to memory constraints. `FeBOLT` allows the use of **32-bit floats** to **significantly reduce memory usage**, while still offering **64-bit float precision** for cases where accuracy is paramount.\n\n### **Inference Without Compromise**\nWhile `scikit-learn` lacks built-in inference tools like **robust and clustered standard errors**, `FeBOLT` incorporates these essential statistical features to support rigorous empirical research. Whether you need **fast OLS regression** or **efficient Probit/Logit estimation with AMEs**, `FeBOLT` delivers both speed and accuracy in one package.\n\n## Installation\n\n```bash\npip install febolt\n```\n\n## Quick Start\n\n```python\nimport febolt\n\n# Example usage (to be filled in)\n```\n\n## Performance\n\n`FeBOLT` outperforms `statsmodels` and `scikit-learn` by leveraging Rust\u2019s speed and memory efficiency. This results in significantly faster execution times, especially for large datasets and models requiring robust standard errors.\n\n## Contributing\n\nContributions are welcome! Feel free to submit issues and pull requests on [GitHub](https://github.com/luke-brosnan-cbc/FeBOLT).\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A Rust-based Statistics and ML package, callable from Python.",
"version": "0.1.1",
"project_urls": {
"Source Code": "https://github.com/luke-brosnan-cbc/RuSTATS"
},
"split_keywords": [
"rust",
" python",
" machine learning",
" statistics",
" pyo3"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "873baf6bbf8f5618faa9456d6e111d78773d70bbf588f6c0217b18a7e3057b1d",
"md5": "7690d5c194866bf70c3ed54a38958956",
"sha256": "ccf371b2da7e545722db9542c46302622a5dfd21c109138bde81dc4fb4b92944"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp310-cp310-macosx_10_12_x86_64.whl",
"has_sig": false,
"md5_digest": "7690d5c194866bf70c3ed54a38958956",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 274516,
"upload_time": "2025-02-16T17:49:21",
"upload_time_iso_8601": "2025-02-16T17:49:21.806310Z",
"url": "https://files.pythonhosted.org/packages/87/3b/af6bbf8f5618faa9456d6e111d78773d70bbf588f6c0217b18a7e3057b1d/rustmfx-0.1.1-cp310-cp310-macosx_10_12_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "68ed0ed4ea1fe553781709c86f4c6a75a57a8d84589a143668f1b18a65a2673a",
"md5": "a71fe45d5b45d4f117379f1ae19d5523",
"sha256": "a14ff715ede8429e152c95ff5a643f020af144c0cf18e989a5236f8ef68f3276"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp310-cp310-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "a71fe45d5b45d4f117379f1ae19d5523",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 255972,
"upload_time": "2025-02-16T17:49:19",
"upload_time_iso_8601": "2025-02-16T17:49:19.898741Z",
"url": "https://files.pythonhosted.org/packages/68/ed/0ed4ea1fe553781709c86f4c6a75a57a8d84589a143668f1b18a65a2673a/rustmfx-0.1.1-cp310-cp310-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "f6e22f5e0a40e783f5f712f317d85caccf7455dc8009d1cf14902357568eeb0d",
"md5": "ccabb29b1d80e420dd64551bb474cfa1",
"sha256": "7b808b5e1d75c614d9c0e019eb458a5fa33d0bc51562d5134e4d4a9c37ae681d"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "ccabb29b1d80e420dd64551bb474cfa1",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 304821,
"upload_time": "2025-02-16T17:49:09",
"upload_time_iso_8601": "2025-02-16T17:49:09.760343Z",
"url": "https://files.pythonhosted.org/packages/f6/e2/2f5e0a40e783f5f712f317d85caccf7455dc8009d1cf14902357568eeb0d/rustmfx-0.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "46c23ad9582894a0a5b65dc7fc25329e2b49c1ef7dea9586c0847c90e0b80ea2",
"md5": "09b5dbcc6b5807f051a8d576f90d34c9",
"sha256": "d7cc35de499bdafde5a38a02b9cbd8fc6574db80fd8ee4904537e59c58acb49f"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp310-cp310-win_amd64.whl",
"has_sig": false,
"md5_digest": "09b5dbcc6b5807f051a8d576f90d34c9",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.8",
"size": 1001635,
"upload_time": "2025-02-16T17:49:35",
"upload_time_iso_8601": "2025-02-16T17:49:35.977917Z",
"url": "https://files.pythonhosted.org/packages/46/c2/3ad9582894a0a5b65dc7fc25329e2b49c1ef7dea9586c0847c90e0b80ea2/rustmfx-0.1.1-cp310-cp310-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "1f48598ed663372d69aa2f7a3f4bceb5640abe5684b23933e432789a9cd1d979",
"md5": "dbeef27067bc7840b801421094e3969b",
"sha256": "0dfd23167b731ebb4de5de8cbe1ca49f442934f7c43abaad6085bb717d8497ff"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp311-cp311-macosx_10_12_x86_64.whl",
"has_sig": false,
"md5_digest": "dbeef27067bc7840b801421094e3969b",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 274514,
"upload_time": "2025-02-16T17:49:24",
"upload_time_iso_8601": "2025-02-16T17:49:24.024717Z",
"url": "https://files.pythonhosted.org/packages/1f/48/598ed663372d69aa2f7a3f4bceb5640abe5684b23933e432789a9cd1d979/rustmfx-0.1.1-cp311-cp311-macosx_10_12_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "54272e87433dce58b703c15e5cd6c5bdf5532ce626da3c08e0080964ec79de26",
"md5": "f5a1002f651e91e505ef8fca8c6ddb8f",
"sha256": "bc0f40b34b0713a0a8ffe877f37a3aa09faee9a85a54fcd2127eb3f0574c69a9"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp311-cp311-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "f5a1002f651e91e505ef8fca8c6ddb8f",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 255971,
"upload_time": "2025-02-16T17:49:22",
"upload_time_iso_8601": "2025-02-16T17:49:22.997749Z",
"url": "https://files.pythonhosted.org/packages/54/27/2e87433dce58b703c15e5cd6c5bdf5532ce626da3c08e0080964ec79de26/rustmfx-0.1.1-cp311-cp311-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "b74278582f745c6d3d693274c5852ba22ece5fb5fde2bfd84c17754851dba770",
"md5": "0c04bee8a28e9676b7bf967ba025158e",
"sha256": "2a09839c15425f3107cabe60ac22b73448630431413599a278affe2057a25d54"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "0c04bee8a28e9676b7bf967ba025158e",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 304822,
"upload_time": "2025-02-16T17:49:11",
"upload_time_iso_8601": "2025-02-16T17:49:11.895642Z",
"url": "https://files.pythonhosted.org/packages/b7/42/78582f745c6d3d693274c5852ba22ece5fb5fde2bfd84c17754851dba770/rustmfx-0.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "b91f7cc19d7bdf007751d371937aa6880a3dc6c38c781abb8cf156c243f06057",
"md5": "81f1f848917d48b782420eaf8319d180",
"sha256": "98d1454a3852d0c3ca4f0779d634d3241551f22a595da0908798de8f70c05c9e"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp311-cp311-win_amd64.whl",
"has_sig": false,
"md5_digest": "81f1f848917d48b782420eaf8319d180",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.8",
"size": 1001640,
"upload_time": "2025-02-16T17:49:37",
"upload_time_iso_8601": "2025-02-16T17:49:37.956838Z",
"url": "https://files.pythonhosted.org/packages/b9/1f/7cc19d7bdf007751d371937aa6880a3dc6c38c781abb8cf156c243f06057/rustmfx-0.1.1-cp311-cp311-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "54d43beffffc4c4e5be01e54f9aca0cb351414bff5c736c4251bac76ee10cbf6",
"md5": "5eb5afb24e89ad63d3e57e7f4fe0c8fa",
"sha256": "9ae368dd88e212c103d04e95ece2791ed4bff130a0b3b231a7e5304d21855b5e"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp312-cp312-macosx_10_12_x86_64.whl",
"has_sig": false,
"md5_digest": "5eb5afb24e89ad63d3e57e7f4fe0c8fa",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 274516,
"upload_time": "2025-02-16T17:49:27",
"upload_time_iso_8601": "2025-02-16T17:49:27.062965Z",
"url": "https://files.pythonhosted.org/packages/54/d4/3beffffc4c4e5be01e54f9aca0cb351414bff5c736c4251bac76ee10cbf6/rustmfx-0.1.1-cp312-cp312-macosx_10_12_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "83a0e33fc25627fc2b6fbf813481f5f1239bacc1148dc0908ff2293139157d7f",
"md5": "eacc9ea42398555ec86a67f1b9227aaa",
"sha256": "8909126cde938d44fc59720c1b8fadcf3fa2c2afcc5d64d83a50137658206f1e"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp312-cp312-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "eacc9ea42398555ec86a67f1b9227aaa",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 255904,
"upload_time": "2025-02-16T17:49:25",
"upload_time_iso_8601": "2025-02-16T17:49:25.188062Z",
"url": "https://files.pythonhosted.org/packages/83/a0/e33fc25627fc2b6fbf813481f5f1239bacc1148dc0908ff2293139157d7f/rustmfx-0.1.1-cp312-cp312-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "ec04981a3187347bc857de0381c1c7cc6969c5fa4deb75bddffb3f3bb473e4e7",
"md5": "c3bd7858b318f5b48a0554002ae7cc54",
"sha256": "ac69598c708dc24220b7996c55b842ef4e083f3a7c40f3c54d66d79cd93188dd"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "c3bd7858b318f5b48a0554002ae7cc54",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 304717,
"upload_time": "2025-02-16T17:49:13",
"upload_time_iso_8601": "2025-02-16T17:49:13.885767Z",
"url": "https://files.pythonhosted.org/packages/ec/04/981a3187347bc857de0381c1c7cc6969c5fa4deb75bddffb3f3bb473e4e7/rustmfx-0.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "00a7368a6068c3884acd241c3eee6e5263b51fe533a2fa7429792c9a7de5b06f",
"md5": "6dd808b138eeb62cc9f75fa942919b5c",
"sha256": "d920fc66850895046b0e47cc5fc4f7404edf24c4af6136c1336afc10ef714c09"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp312-cp312-win_amd64.whl",
"has_sig": false,
"md5_digest": "6dd808b138eeb62cc9f75fa942919b5c",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.8",
"size": 1001544,
"upload_time": "2025-02-16T17:49:39",
"upload_time_iso_8601": "2025-02-16T17:49:39.109797Z",
"url": "https://files.pythonhosted.org/packages/00/a7/368a6068c3884acd241c3eee6e5263b51fe533a2fa7429792c9a7de5b06f/rustmfx-0.1.1-cp312-cp312-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "ba324aa584d93342a578c03156a1142f70dcaefe72b00b308aa76672bc15f287",
"md5": "e5a99f9ed07fcca917575c674e334694",
"sha256": "5ae5aebc97375cc5edfb553256e9e91e8d777a84e059656881187ddb334aef15"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp38-cp38-macosx_10_12_x86_64.whl",
"has_sig": false,
"md5_digest": "e5a99f9ed07fcca917575c674e334694",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 274329,
"upload_time": "2025-02-16T17:49:30",
"upload_time_iso_8601": "2025-02-16T17:49:30.153583Z",
"url": "https://files.pythonhosted.org/packages/ba/32/4aa584d93342a578c03156a1142f70dcaefe72b00b308aa76672bc15f287/rustmfx-0.1.1-cp38-cp38-macosx_10_12_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "0a0f619996cf3e9111c4c383d90e366abef38f53146581c613b707e536852300",
"md5": "cfd7d623e892905e0426b7f5ae11f11e",
"sha256": "60792ec10786cbf6e3d70d74520ba81faa1a9d3e4976329fa04ef9a9b3b881f0"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp38-cp38-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "cfd7d623e892905e0426b7f5ae11f11e",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 255746,
"upload_time": "2025-02-16T17:49:28",
"upload_time_iso_8601": "2025-02-16T17:49:28.855797Z",
"url": "https://files.pythonhosted.org/packages/0a/0f/619996cf3e9111c4c383d90e366abef38f53146581c613b707e536852300/rustmfx-0.1.1-cp38-cp38-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "2f102f3ca44225cc53f2f3b39a329a8c7a6d18ab9d9dc1a370d50bf90711aef2",
"md5": "832cee622ac0ad2928b951ccb6c77b5c",
"sha256": "e2670cfaa8a6b3f34526c482b727d099d475348b4ffd90e27ff57847ce44c62d"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "832cee622ac0ad2928b951ccb6c77b5c",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 304509,
"upload_time": "2025-02-16T17:49:16",
"upload_time_iso_8601": "2025-02-16T17:49:16.041792Z",
"url": "https://files.pythonhosted.org/packages/2f/10/2f3ca44225cc53f2f3b39a329a8c7a6d18ab9d9dc1a370d50bf90711aef2/rustmfx-0.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "0a5d8b336f1342c36cd91358614c96b3cb349937f95c0fa4aa78908d7cce4a6a",
"md5": "7f54dd54c3c5c67ff951e8c9379b9a59",
"sha256": "4ed02051f01595473714b451a1a395581b9863fbc6c427c72ad6daf5a6549811"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp38-cp38-win_amd64.whl",
"has_sig": false,
"md5_digest": "7f54dd54c3c5c67ff951e8c9379b9a59",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.8",
"size": 1001494,
"upload_time": "2025-02-16T17:49:40",
"upload_time_iso_8601": "2025-02-16T17:49:40.285973Z",
"url": "https://files.pythonhosted.org/packages/0a/5d/8b336f1342c36cd91358614c96b3cb349937f95c0fa4aa78908d7cce4a6a/rustmfx-0.1.1-cp38-cp38-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "8ab897b5496a67e8ba4b18eb24ff043330bde2de4fb562c94590eb092fa4feb0",
"md5": "c1458084a8f5a2645900d0661a3d9a64",
"sha256": "37f49b55f8a103ea1deb21b858c36ac00c94beb1b5bcd9939af0d2efeaa73be4"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp39-cp39-macosx_10_12_x86_64.whl",
"has_sig": false,
"md5_digest": "c1458084a8f5a2645900d0661a3d9a64",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 274473,
"upload_time": "2025-02-16T17:49:33",
"upload_time_iso_8601": "2025-02-16T17:49:33.164836Z",
"url": "https://files.pythonhosted.org/packages/8a/b8/97b5496a67e8ba4b18eb24ff043330bde2de4fb562c94590eb092fa4feb0/rustmfx-0.1.1-cp39-cp39-macosx_10_12_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "396c76c374e8a403f27749f5ec88b685c9e1f6276197f96d49ff7fc04651fb2e",
"md5": "c332c3f59c92b747df95707ab4a7abf6",
"sha256": "517148045e190931d35ea7c0eb37bd492b083bbb734bbe3b9f983945166a3d07"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp39-cp39-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "c332c3f59c92b747df95707ab4a7abf6",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 255831,
"upload_time": "2025-02-16T17:49:31",
"upload_time_iso_8601": "2025-02-16T17:49:31.336385Z",
"url": "https://files.pythonhosted.org/packages/39/6c/76c374e8a403f27749f5ec88b685c9e1f6276197f96d49ff7fc04651fb2e/rustmfx-0.1.1-cp39-cp39-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "32a0e5f69142e938506070f740134423051dd31541df4706790fa104dfbbf731",
"md5": "81b6c62e967e89824b4014645f966cfe",
"sha256": "dbd1a0b2a4d64a3c7d89688e91f8fef3af683256a386eaef083e1c89bfe688b1"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "81b6c62e967e89824b4014645f966cfe",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 304592,
"upload_time": "2025-02-16T17:49:17",
"upload_time_iso_8601": "2025-02-16T17:49:17.978922Z",
"url": "https://files.pythonhosted.org/packages/32/a0/e5f69142e938506070f740134423051dd31541df4706790fa104dfbbf731/rustmfx-0.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "42be41078d7a739bc93770af3c519c33491aa36e1f2ec87b92a3bf2d1cdc1cd6",
"md5": "cb0d7cbfda6d75ebe6fe025a81e2a439",
"sha256": "cd1dda887026d1c3b671c4038b288f24c37913df0af3a05c0f3508bf76529964"
},
"downloads": -1,
"filename": "rustmfx-0.1.1-cp39-cp39-win_amd64.whl",
"has_sig": false,
"md5_digest": "cb0d7cbfda6d75ebe6fe025a81e2a439",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.8",
"size": 1001649,
"upload_time": "2025-02-16T17:49:41",
"upload_time_iso_8601": "2025-02-16T17:49:41.486816Z",
"url": "https://files.pythonhosted.org/packages/42/be/41078d7a739bc93770af3c519c33491aa36e1f2ec87b92a3bf2d1cdc1cd6/rustmfx-0.1.1-cp39-cp39-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "adbe26eeefc9eaf4bace611486f65b601c801b513a54bff807da6926531bdd47",
"md5": "2cd441dc4f83dde2e454864d08a9aa2b",
"sha256": "a5cd885759ae23972a494b3af833dd5c071e65e744e1c67eaea2229092174c90"
},
"downloads": -1,
"filename": "rustmfx-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "2cd441dc4f83dde2e454864d08a9aa2b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 27086,
"upload_time": "2025-02-16T17:49:34",
"upload_time_iso_8601": "2025-02-16T17:49:34.307111Z",
"url": "https://files.pythonhosted.org/packages/ad/be/26eeefc9eaf4bace611486f65b601c801b513a54bff807da6926531bdd47/rustmfx-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-16 17:49:34",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "luke-brosnan-cbc",
"github_project": "RuSTATS",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "rustmfx"
}