A framework for estimating and applying discrete choice models.
Project description
Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models. It is designed to integrate with NumPy and facilitate fast processing of linear models. If you want to estimate non-linear models, try Biogeme, which is more flexible in form and can be used for almost any model structure. If you don’t know what the difference is, you probably want to start with linear models.
This project is very much under development. There are plenty of undocumented functions and features; use them at your own risk. Undocumented features may be non-functional, not rigorously tested, deprecated or removed without notice in a future version. If a function or method is documented, it is intended to be stable in future updates.
FAQ
Why is the Windows download so much larger than the Mac download?
The Windows wheel include the openblas library for linear algebra computations. The Mac version does not need an extra library because Mac OS X includes vector math libraries by default.
It is not working. Can you troubleshoot for me?
Are you using the 64 bit (amd64) version of Python? Larch is only compiled for 64 bit at present.
For some unknown reason, certain mathematical tools are not available on PyPI as wheels for Windows. You will need to download numpy, scipy, and pandas and install them manually.
You may also need to install the Microsoft Visual C++ 2015 <https://www.microsoft.com/en-us/download/details.aspx?id=48145> redistributable libraries. Future versions of Larch may include these for you.
Release history Release notifications
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
| Filename, size | File type | Python version | Upload date | Hashes |
|---|---|---|---|---|
| Filename, size larch-3.3.24-cp35-cp35m-macosx_10_6_x86_64.whl (5.7 MB) | File type Wheel | Python version cp35 | Upload date | Hashes View hashes |
| Filename, size larch-3.3.24-cp35-cp35m-win_amd64.whl (15.6 MB) | File type Wheel | Python version cp35 | Upload date | Hashes View hashes |
| Filename, size larch-3.3.24-cp36-cp36m-macosx_10_7_x86_64.whl (5.7 MB) | File type Wheel | Python version cp36 | Upload date | Hashes View hashes |
| Filename, size larch-3.3.24-cp36-cp36m-win_amd64.whl (15.1 MB) | File type Wheel | Python version cp36 | Upload date | Hashes View hashes |
Hashes for larch-3.3.24-cp35-cp35m-macosx_10_6_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 9fc82e4734c3996cbe93b17b099c3ebe6eb1f38c0a505ead6c2a166524aa199a |
|
| MD5 | 17772635a36b30d3d83f6911bc80a2b5 |
|
| BLAKE2-256 | 5acdfc2429b2ba6360a6a59182a0dc75df2154adc9b3d0d90fbe09e96d144b76 |
Hashes for larch-3.3.24-cp35-cp35m-win_amd64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 3b85a9357f1e9752f5c2a809ff13b35e620b2ec7765adf15ef3964b7038163e1 |
|
| MD5 | 1ac7ce8a2807519605a9faa5e3cf2053 |
|
| BLAKE2-256 | 7604223694988436edfa8a84c9c26c649476be9192e62eea5db9e1a8124fbad0 |
Hashes for larch-3.3.24-cp36-cp36m-macosx_10_7_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f9bea78bc739d030ed3ca204f59196c4fd945a7aba65223d849450ccf91b6ad7 |
|
| MD5 | 7100859f470d861411c1fc7f98af6ae2 |
|
| BLAKE2-256 | 911dbd5ee6b2b7169f4e85c98273d4bd6f1ca6c6a5d20ef4289b9bc89781a2b8 |
Hashes for larch-3.3.24-cp36-cp36m-win_amd64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 140561c091106f163d7486d83e603c16f492f2b3865e977f911a64c029a3cc83 |
|
| MD5 | b99c82da1e42fbd6dcc213f38e2c9a66 |
|
| BLAKE2-256 | 8a43da2fefb010616156a2e339872b91b24f805215ed1ae8d4ab13ef35664e5a |
