- switch upstream to GitHub
- as there is no new release available, bump PORTREVISION
- remove Sphinx build from docs option, due to recursive imports.
- update to 0.6.1 and unbreak
- replace noegginfo with concurrent in USE_PYTHON
Differential Revision: https://reviews.freebsd.org/D1287
Approved by: swills (mentor)
building [html]: targets for 66 source files that are out of date
updating environment: 1739 added, 0 changed, 0 removed
reading sources... [ 0%] anova
Traceback (most recent call last):
...
File "/wrkdirs/usr/ports/math/py-statsmodels/work/statsmodels-0.5.0/docs/sphinxext/ipython_directive.py", line 589, in setup
store_history=False)
File "/wrkdirs/usr/ports/math/py-statsmodels/work/statsmodels-0.5.0/docs/sphinxext/ipython_directive.py", line 260, in process_input_line
source_raw = splitter.source_raw_reset()[1]
AttributeError: 'IPythonInputSplitter' object has no attribute 'source_raw_reset'
*** [do-build] Error code 1
Reported by: pkg-fallout
GCC 4.6.4 to GCC 4.7.3. This entails updating the lang/gcc port as
well as changing the default in Mk/bsd.default-versions.mk.
Part II, Bump PORTREVISIONs.
PR: 182136
Supported by: Christoph Moench-Tegeder <cmt@burggraben.net> (fixing many ports)
Tested by: bdrewery (two -exp runs)
Statsmodels is a Python package that provides a complement to scipy for
statistical computations including descriptive statistics and estimation and
inference for statistical models.
Main Features:
* linear regression models: GLS (including WLS and LS aith AR errors) and OLS.
* glm: Generalized linear models with support for all of the one-parameter
exponential family distributions.
* discrete: regression with discrete dependent variables, including Logit,
Probit, MNLogit, Poisson, based on maximum likelihood estimators
* rlm: Robust linear models with support for several M-estimators.
* tsa: models for time series analysis - univariate: AR, ARIMA; multivariate:
VAR and structural VAR
* nonparametric: (Univariate) kernel density estimators
* datasets: Datasets to be distributed and used for examples and in testing.
* stats: a wide range of statistical tests, diagnostics and specification tests
* iolib: Tools for reading Stata .dta files into numpy arrays, printing table
output to ascii, latex, and html
* miscellaneous models
* sandbox: statsmodels contains a sandbox folder with code in various stages of
* developement and testing which is not considered "production ready", including
Mixed models, GARCH and GMM estimators, kernel regression, panel data models.
WWW: https://www.github.com/statsmodels/statsmodels
PR: ports/183932
Submitted by: Johannes Jost Meixner <xmj chaot.net>
