Notes from UPDATING |
These upgrade notes are taken from /usr/ports/UPDATING
- 2017-11-30
Affects: */py* Author: mat@FreeBSD.org Reason:
Ports using Python via USES=python are now flavored. All the py3-* ports
have been removed and folded into their py-* master ports.
People using Poudriere 3.2+ and binary packages do not have to do anything.
For other people, to build the Python 3.6 version of, for example,
databases/py-gdbm, you need to run:
# make FLAVOR=py36 install
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Number of commits found: 1
Commit History - (may be incomplete: see SVNWeb link above for full details) |
Date | By | Description |
03 Jan 2021 19:57:28
1.0.8

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sunpoet  |
Add py-spglm 1.0.8
This module is an adaptation of a portion of GLM functionality from the
Statsmodels package, this it has been simplified and customized for the purposes
of serving as the base for several other PySAL modules, namely SpInt and GWR.
Currently, it supports the estimation of Gaussian, Poisson, and Logistic
regression using only iteratively weighted least squares estimation (IWLS). One
of the large differences this module and the functions avaialble in the
Statsmodels package is that the custom IWLS routine is fully sparse compatible,
which was necesary for the very sparse design matrices that arise in constrained
spatial interaction models. The somewhat limited functionality and computation
of only a subset of GLM diagnostics also decreases the computational overhead.
Another difference is that this module also supports the estimation of
QuasiPoisson models. One caveat is that this custom IWLS routine currently
generates estimates by directly solves the least squares normal equations rather
than using a more robust method like the pseudo inverse. For more robust
estimation of ill conditioned data and a fuller GLM framework we suggest using
the original GLM functionality from Statsmodels.
WWW: https://github.com/pysal/spglm |
Number of commits found: 1
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Latest Vulnerabilities |
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* - modified, not new All vulnerabilities
Last updated: 2021-01-23 18:22:23
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