PyKernelLogit

What is PyKernelLogit

PyKernelLogit is a Python package for performing maximum likelihood estimation of conditional logit models and similar discrete choice models based on the Python package PyLogit. This package extends the functionalities of PyLogit to provide some functionalities that allows to estimate discrete choice models based on Kernel Logistic Regression (KLR). Moreover, this package provides some functions to estimate indicators such as the Willingness to Pay (WTP) for the KLR models.

Installation

PyKernelLogit is available for its installation on the Python Package Index at: https://pypi.org/project/pykernellogit/.

Authors

PyKernelLogit was developed by José Ángel Martín Baos from the University of Castilla-La Mancha (JoseAngel.Martin@uclm.es) based on the package PyLogit (https://github.com/timothyb0912/pylogit) developed by Timothy Brathwaite.

License

Modified BSD (3-clause)

Metadata

Release files for pykernellogit 0.1.7

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Source distribution (sdist)

Source distribution for pykernellogit 0.1.7
File	Size	Uploaded	Provenance
pykernellogit-0.1.7.tar.gz	144.4 kB	Feb 28, 2020	Without provenance	Details

Built distribution (wheel)

Table of built distributions (wheels) for pykernellogit 0.1.7
File	Interpreter	ABI	Platform	Reset
pykernellogit-0.1.7-py3-none-any.whl 165.3 kB Feb 28, 2020 Without provenance	Python 3	none	any	Details

Total release size: 309.7 kB

Release files / pykernellogit-0.1.7.tar.gz

Download URL	pykernellogit-0.1.7.tar.gz
Size	144.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`09ba07353ea7b088abba4dace9ff23335c47b1ecae6074ffd8bb1fa081663c4b`
BLAKE2b-256 checksum How to use checksums	`8fb948e71c1a395bd84871a35cc13f0ac1b08b8ce66ae2aad0a6d106cfee7f6f`
Upload date	Feb 28, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.9`

Release files / pykernellogit-0.1.7-py3-none-any.whl

Download URL	pykernellogit-0.1.7-py3-none-any.whl
Size	165.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`a93771c426e7897e44de25a54231db0ca25849390a74fc9aed4dd159d051acc4`
BLAKE2b-256 checksum How to use checksums	`60298e51340cf338c576d99863a581d74b480b1d12d71b0d86a4a461967514b3`
Upload date	Feb 28, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.9`

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