From aa35043867667f61977de0e1bc63d5cc057dfc35 Mon Sep 17 00:00:00 2001 From: Debian Science Maintainers Date: Tue, 28 Jan 2020 22:29:29 +0000 Subject: [PATCH] Change some more links to https Author: Rebecca N. Palmer Forwarded: https://github.com/statsmodels/statsmodels/pull/5937 Gbp-Pq: Name link_security2.patch --- CONTRIBUTING.rst | 8 ++++---- INSTALL.txt | 8 ++++---- README.rst | 2 +- docs/source/_static/mktree.js | 2 +- docs/source/dev/git_notes.rst | 6 +++--- docs/source/dev/test_notes.rst | 2 +- docs/source/diagnostic.rst | 2 +- docs/source/example_formulas.rst | 8 ++++---- docs/source/gettingstarted.rst | 6 +++--- docs/source/imputation.rst | 2 +- docs/source/install.rst | 16 ++++++++-------- docs/source/release/version0.5.rst | 2 +- docs/source/sandbox.rst | 2 +- docs/source/vector_ar.rst | 2 +- docs/themes/statsmodels/sidelinks.html | 2 +- examples/notebooks/formulas.ipynb | 2 +- examples/notebooks/generic_mle.ipynb | 2 +- .../statespace_structural_harvey_jaeger.ipynb | 2 +- examples/python/formulas.py | 2 +- examples/python/generic_mle.py | 2 +- statsmodels/distributions/edgeworth.py | 2 +- statsmodels/examples/ex_outliers_influence.py | 6 +++--- statsmodels/graphics/functional.py | 2 +- statsmodels/graphics/plot_grids.py | 2 +- statsmodels/nonparametric/kde.py | 2 +- statsmodels/nonparametric/kernel_density.py | 4 ++-- statsmodels/regression/linear_model.py | 2 +- statsmodels/regression/recursive_ls.py | 2 +- statsmodels/sandbox/distributions/extras.py | 4 ++-- statsmodels/sandbox/distributions/otherdist.py | 2 +- statsmodels/sandbox/distributions/sppatch.py | 4 ++-- .../regression/kernridgeregress_class.py | 2 +- statsmodels/sandbox/stats/diagnostic.py | 2 +- statsmodels/sandbox/stats/runs.py | 2 +- statsmodels/sandbox/tsa/varma.py | 4 ++-- statsmodels/stats/contingency_tables.py | 2 +- statsmodels/stats/inter_rater.py | 6 +++--- statsmodels/stats/moment_helpers.py | 2 +- statsmodels/stats/outliers_influence.py | 6 +++--- statsmodels/stats/proportion.py | 2 +- statsmodels/tools/eval_measures.py | 12 ++++++------ statsmodels/tools/numdiff.py | 4 ++-- statsmodels/tsa/statespace/structural.py | 2 +- statsmodels/tsa/stattools.py | 2 +- tools/R2nparray/DESCRIPTION | 2 +- tools/matplotlibrc.qt4 | 4 ++-- tools/matplotlibrc.qt5 | 18 +++++++++--------- tools/notebook_output_template.py | 2 +- 48 files changed, 93 insertions(+), 93 deletions(-) diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst index f51c186..eae5469 100644 --- a/CONTRIBUTING.rst +++ b/CONTRIBUTING.rst @@ -5,7 +5,7 @@ This page explains how you can contribute to the development of `statsmodels` by submitting patches, statistical tests, new models, or examples. `statsmodels` is developed on `Github `_ -using the `Git `_ version control system. +using the `Git `_ version control system. Submitting a Bug Report ~~~~~~~~~~~~~~~~~~~~~~~ @@ -49,7 +49,7 @@ How to Submit a Pull Request So you want to submit a patch to `statsmodels` but aren't too familiar with github? Here are the steps you need to take. 1. `Fork `_ the `statsmodels repository `_ on Github. -2. `Create a new feature branch `_. Each branch must be self-contained, with a single new feature or bugfix. +2. `Create a new feature branch `_. Each branch must be self-contained, with a single new feature or bugfix. 3. Make sure the test suite passes. This includes testing on Python 3. The easiest way to do this is to either enable `Travis-CI `_ on your fork, or to make a pull request and check there. 4. Document your changes by editing the appropriate file in ``docs/source/``. If it is a big, new feature add a note and an example to the latest ``docs/source/release/versionX.X.rst`` file. See older versions for examples. If it's a minor change, it will be included automatically in our relase notes. 5. Add an example. If it is a big, new feature please submit an example notebook by following `these instructions `_. @@ -58,7 +58,7 @@ So you want to submit a patch to `statsmodels` but aren't too familiar with gith Mailing List ~~~~~~~~~~~~ -Conversations about development take place on the `statsmodels mailing list `__. +Conversations about development take place on the `statsmodels mailing list `__. Learn More ~~~~~~~~~~ @@ -70,4 +70,4 @@ License ~~~~~~~ Statsmodels is released under the -`Modified (3-clause) BSD license `_. +`Modified (3-clause) BSD license `_. diff --git a/INSTALL.txt b/INSTALL.txt index f0b0b36..89ba992 100644 --- a/INSTALL.txt +++ b/INSTALL.txt @@ -23,7 +23,7 @@ patsy >= 0.4.0 cython >= 0.24 - http://cython.org/ + https://cython.org/ Cython is required if you are building the source from github. However, if you have are building from source distribution archive then the @@ -36,7 +36,7 @@ Optional Dependencies X-12-ARIMA or X-13ARIMA-SEATS - http://www.census.gov/srd/www/x13as/ + https://www.census.gov/srd/www/x13as/ If available, time-series analysis can be conducted using either X-12-ARIMA or the newer X-13ARIMA-SEATS. You should place the @@ -45,7 +45,7 @@ X-12-ARIMA or X-13ARIMA-SEATS matplotlib >= 1.5 - http://matplotlib.org/ + https://matplotlib.org/ Matplotlib is needed for plotting functionality and running many of the examples. @@ -95,7 +95,7 @@ Installing from Source Download and extract the source distribution from PyPI or github - http://pypi.python.org/pypi/statsmodels + https://pypi.python.org/pypi/statsmodels https://github.com/statsmodels/statsmodels/tags Or clone the bleeding edge code from our repository on github at diff --git a/README.rst b/README.rst index a073896..79a70fd 100644 --- a/README.rst +++ b/README.rst @@ -149,7 +149,7 @@ Discussion and Development Discussions take place on our mailing list. -http://groups.google.com/group/pystatsmodels +https://groups.google.com/group/pystatsmodels We are very interested in feedback about usability and suggestions for improvements. diff --git a/docs/source/_static/mktree.js b/docs/source/_static/mktree.js index 299cb2a..6322c5e 100644 --- a/docs/source/_static/mktree.js +++ b/docs/source/_static/mktree.js @@ -4,7 +4,7 @@ * Dual licensed under the MIT and GPL licenses. * This basically means you can use this code however you want for * free, but don't claim to have written it yourself! - * Donations always accepted: http://www.JavascriptToolbox.com/donate/ + * Donations always accepted: https://www.JavascriptToolbox.com/donate/ * * Please do not link to the .js files on javascripttoolbox.com from * your site. Copy the files locally to your server instead. diff --git a/docs/source/dev/git_notes.rst b/docs/source/dev/git_notes.rst index bce8357..5062c4a 100644 --- a/docs/source/dev/git_notes.rst +++ b/docs/source/dev/git_notes.rst @@ -12,7 +12,7 @@ contribute you will need to `sign up for a free Github account `_ version control system for development. +We use the `Git `_ version control system for development. Git allows many people to work together on the same project. In a nutshell, it allows you to make changes to the code independent of others who may also be working on the code and allows you to easily contribute your changes to the @@ -27,7 +27,7 @@ To learn more about Git, you may want to visit: + `Git documentation (book and videos) `_ + `Github help pages `_ + `NumPy documentation `_ -+ `Matthew Brett's Pydagogue `_ ++ `Matthew Brett's Pydagogue `_ Below, we describe the bare minimum git commands you need to contribute to `statsmodels`. @@ -217,7 +217,7 @@ could introduce bugs. However, if you have only a few commits, this might not be such a concern. One great place to start learning about rebase is :ref:`rebasing without tears `. In particular, `heed the warnings -`__. +`__. Namely, **always make a new branch before doing a rebase**. This is good general advice for working with git. I would also add **never use rebase on work that has already been published**. If another developer is using your diff --git a/docs/source/dev/test_notes.rst b/docs/source/dev/test_notes.rst index 5bcf240..c8a1209 100644 --- a/docs/source/dev/test_notes.rst +++ b/docs/source/dev/test_notes.rst @@ -24,7 +24,7 @@ tests versus an existing statistical package, if possible. Introduction to pytest ---------------------- -Like many packages, statsmodels uses the `pytest testing system `__ and the convenient extensions in `numpy.testing `__. Pytest will find any file, directory, function, or class name that starts with ``test`` or ``Test`` (classes only). Test function should start with ``test``, test classes should start with ``Test``. These functions and classes should be placed in files with names beginning with ``test`` in a directory called ``tests``. +Like many packages, statsmodels uses the `pytest testing system `__ and the convenient extensions in `numpy.testing `__. Pytest will find any file, directory, function, or class name that starts with ``test`` or ``Test`` (classes only). Test function should start with ``test``, test classes should start with ``Test``. These functions and classes should be placed in files with names beginning with ``test`` in a directory called ``tests``. .. _run-tests: diff --git a/docs/source/diagnostic.rst b/docs/source/diagnostic.rst index f555b5a..9d9f104 100644 --- a/docs/source/diagnostic.rst +++ b/docs/source/diagnostic.rst @@ -210,7 +210,7 @@ individual outliers and might not be able to identify groups of outliers. - resid_studentized_internal - ess_press - hat_matrix_diag - - cooks_distance - Cook's Distance `Wikipedia `_ (with some other links) + - cooks_distance - Cook's Distance `Wikipedia `_ (with some other links) - cov_ratio - dfbetas - dffits diff --git a/docs/source/example_formulas.rst b/docs/source/example_formulas.rst index b950e4d..a9b1439 100644 --- a/docs/source/example_formulas.rst +++ b/docs/source/example_formulas.rst @@ -5,13 +5,13 @@ Fitting models using R-style formulas Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the -`patsy `_ package to convert formulas and +`patsy `_ package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the ``patsy`` docs: -- `Patsy formula language description `_ +- `Patsy formula language description `_ Loading modules and functions ----------------------------- @@ -31,7 +31,7 @@ counterparts for most of these models. In general, lower case models accept ``formula`` and ``df`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``df`` takes -a `pandas `_ data frame. +a `pandas `_ data frame. ``dir(smf)`` will print a list of available models. @@ -145,7 +145,7 @@ Define a custom function: Namespaces ---------- -Notice that all of the above examples use the calling namespace to look for the functions to apply. The namespace used can be controlled via the ``eval_env`` keyword. For example, you may want to give a custom namespace using the :class:`patsy:patsy.EvalEnvironment` or you may want to use a "clean" namespace, which we provide by passing ``eval_func=-1``. The default is to use the caller's namespace. This can have (un)expected consequences, if, for example, someone has a variable names ``C`` in the user namespace or in their data structure passed to ``patsy``, and ``C`` is used in the formula to handle a categorical variable. See the `Patsy API Reference `_ for more information. +Notice that all of the above examples use the calling namespace to look for the functions to apply. The namespace used can be controlled via the ``eval_env`` keyword. For example, you may want to give a custom namespace using the :class:`patsy:patsy.EvalEnvironment` or you may want to use a "clean" namespace, which we provide by passing ``eval_func=-1``. The default is to use the caller's namespace. This can have (un)expected consequences, if, for example, someone has a variable names ``C`` in the user namespace or in their data structure passed to ``patsy``, and ``C`` is used in the formula to handle a categorical variable. See the `Patsy API Reference `_ for more information. Using formulas with models that do not (yet) support them --------------------------------------------------------- diff --git a/docs/source/gettingstarted.rst b/docs/source/gettingstarted.rst index ff27ab6..f2a56d5 100644 --- a/docs/source/gettingstarted.rst +++ b/docs/source/gettingstarted.rst @@ -20,7 +20,7 @@ few modules and functions: import pandas from patsy import dmatrices -`pandas `_ builds on ``numpy`` arrays to provide +`pandas `_ builds on ``numpy`` arrays to provide rich data structures and data analysis tools. The ``pandas.DataFrame`` function provides labelled arrays of (potentially heterogenous) data, similar to the ``R`` "data.frame". The ``pandas.read_csv`` function can be used to convert a @@ -94,7 +94,7 @@ capita (*Lottery*). :math:`X` is :math:`N \times 7` with an intercept, the *Literacy* and *Wealth* variables, and 4 region binary variables. The ``patsy`` module provides a convenient function to prepare design matrices -using ``R``-like formulas. You can find more information `here `_. +using ``R``-like formulas. You can find more information `here `_. We use ``patsy``'s ``dmatrices`` function to create design matrices: @@ -116,7 +116,7 @@ Notice that ``dmatrices`` has * returned ``pandas`` DataFrames instead of simple numpy arrays. This is useful because DataFrames allow ``statsmodels`` to carry-over meta-data (e.g. variable names) when reporting results. The above behavior can of course be altered. See the `patsy doc pages -`_. +`_. Model fit and summary --------------------- diff --git a/docs/source/imputation.rst b/docs/source/imputation.rst index c7efcce..f2d24a5 100644 --- a/docs/source/imputation.rst +++ b/docs/source/imputation.rst @@ -48,5 +48,5 @@ Implementation Details ---------------------- Internally, this function uses -`pandas.isnull `_. +`pandas.isnull `_. Anything that returns True from this function will be treated as missing data. diff --git a/docs/source/install.rst b/docs/source/install.rst index 4171c1b..a31df09 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -104,11 +104,11 @@ Dependencies The current minimum dependencies are: * `Python `__ >= 2.7, including Python 3.4+ -* `NumPy `__ >= 1.11 -* `SciPy `__ >= 0.18 -* `Pandas `__ >= 0.19 +* `NumPy `__ >= 1.11 +* `SciPy `__ >= 0.18 +* `Pandas `__ >= 0.19 * `Patsy `__ >= 0.4.0 -* `Cython `__ >= 0.24 is required to build the code from +* `Cython `__ >= 0.24 is required to build the code from github but not from a source distribution. Given the long release cycle, Statsmodels follows a loose time-based policy for @@ -121,14 +121,14 @@ September 2018, when we will update to reflect Numpy >= 1.12 (released January Optional Dependencies --------------------- -* `Matplotlib `__ >= 1.5 is needed for plotting +* `Matplotlib `__ >= 1.5 is needed for plotting functions and running many of the examples. -* If installed, `X-12-ARIMA `__ or - `X-13ARIMA-SEATS `__ can be used +* If installed, `X-12-ARIMA `__ or + `X-13ARIMA-SEATS `__ can be used for time-series analysis. * `pytest `__ is required to run the test suite. -* `IPython `__ >= 3.0 is required to build the +* `IPython `__ >= 3.0 is required to build the docs locally or to use the notebooks. * `joblib `__ >= 0.9 can be used to accelerate distributed estimation for certain models. diff --git a/docs/source/release/version0.5.rst b/docs/source/release/version0.5.rst index c346b82..8b5ba98 100644 --- a/docs/source/release/version0.5.rst +++ b/docs/source/release/version0.5.rst @@ -69,7 +69,7 @@ It is now possible to fit negative binomial models for count data via maximum-li l1-penalized Discrete Choice Models ----------------------------------- -A new optimization method has been added to the discrete models, which includes Logit, Probit, MNLogit and Poisson, that makes it possible to estimate the models with an l1, linear, penalization. This shrinks parameters towards zero and can set parameters that are not very different from zero to zero. This is especially useful if there are a large number of explanatory variables and a large associated number of parameters. `CVXOPT `_ is now an optional dependency that can be used for fitting these models. +A new optimization method has been added to the discrete models, which includes Logit, Probit, MNLogit and Poisson, that makes it possible to estimate the models with an l1, linear, penalization. This shrinks parameters towards zero and can set parameters that are not very different from zero to zero. This is especially useful if there are a large number of explanatory variables and a large associated number of parameters. `CVXOPT `_ is now an optional dependency that can be used for fitting these models. New and Improved Graphics ------------------------- diff --git a/docs/source/sandbox.rst b/docs/source/sandbox.rst index 22f7eb0..392f17a 100644 --- a/docs/source/sandbox.rst +++ b/docs/source/sandbox.rst @@ -43,7 +43,7 @@ Moving Window Statistics Most moving window statistics, like rolling mean, moments (up to 4th order), min, max, mean, and variance, are covered by the functions for `Moving (rolling) -statistics/moments `_ in Pandas. +statistics/moments `_ in Pandas. .. module:: statsmodels.sandbox.tsa :synopsis: Experimental time-series analysis models diff --git a/docs/source/vector_ar.rst b/docs/source/vector_ar.rst index 08a8b72..3efc2fc 100644 --- a/docs/source/vector_ar.rst +++ b/docs/source/vector_ar.rst @@ -34,7 +34,7 @@ and their lagged values is the *vector autoregression process*: where :math:`A_i` is a :math:`K \times K` coefficient matrix. We follow in large part the methods and notation of `Lutkepohl (2005) -`__, +`__, which we will not develop here. Model fitting diff --git a/docs/themes/statsmodels/sidelinks.html b/docs/themes/statsmodels/sidelinks.html index 4ac8089..d49066c 100644 --- a/docs/themes/statsmodels/sidelinks.html +++ b/docs/themes/statsmodels/sidelinks.html @@ -1,2 +1,2 @@

Twitter -Blog

+Blog

diff --git a/examples/notebooks/formulas.ipynb b/examples/notebooks/formulas.ipynb index dae2e8f..404d3c9 100644 --- a/examples/notebooks/formulas.ipynb +++ b/examples/notebooks/formulas.ipynb @@ -114,7 +114,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "All of the lower case models accept ``formula`` and ``data`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``data`` takes a [pandas](http://pandas.pydata.org/) data frame or any other data structure that defines a ``__getitem__`` for variable names like a structured array or a dictionary of variables. \n", + "All of the lower case models accept ``formula`` and ``data`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``data`` takes a [pandas](https://pandas.pydata.org/) data frame or any other data structure that defines a ``__getitem__`` for variable names like a structured array or a dictionary of variables. \n", "\n", "``dir(sm.formula)`` will print a list of available models. \n", "\n", diff --git a/examples/notebooks/generic_mle.ipynb b/examples/notebooks/generic_mle.ipynb index 47b2ebc..8b5849c 100644 --- a/examples/notebooks/generic_mle.ipynb +++ b/examples/notebooks/generic_mle.ipynb @@ -290,7 +290,7 @@ "\n", "The [Medpar](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/doc/COUNT/medpar.html)\n", "dataset is hosted in CSV format at the [Rdatasets repository](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets). We use the ``read_csv``\n", - "function from the [Pandas library](http://pandas.pydata.org) to load the data\n", + "function from the [Pandas library](https://pandas.pydata.org) to load the data\n", "in memory. We then print the first few columns: \n" ] }, diff --git a/examples/notebooks/statespace_structural_harvey_jaeger.ipynb b/examples/notebooks/statespace_structural_harvey_jaeger.ipynb index 00674e3..467bc83 100644 --- a/examples/notebooks/statespace_structural_harvey_jaeger.ipynb +++ b/examples/notebooks/statespace_structural_harvey_jaeger.ipynb @@ -164,7 +164,7 @@ "\n", "The time frame in the original paper varied across series, but was broadly 1954-1989. Below we use data from the period 1948-2008 for all series. Although the unobserved components approach allows isolating a seasonal component within the model, the series considered in the paper, and here, are already seasonally adjusted.\n", "\n", - "All data series considered here are taken from [Federal Reserve Economic Data (FRED)](https://research.stlouisfed.org/fred2/). Conveniently, the Python library [Pandas](http://pandas.pydata.org/) has the ability to download data from FRED directly." + "All data series considered here are taken from [Federal Reserve Economic Data (FRED)](https://research.stlouisfed.org/fred2/). Conveniently, the Python library [Pandas](https://pandas.pydata.org/) has the ability to download data from FRED directly." ] }, { diff --git a/examples/python/formulas.py b/examples/python/formulas.py index 7d922e3..172bec2 100644 --- a/examples/python/formulas.py +++ b/examples/python/formulas.py @@ -47,7 +47,7 @@ sm.OLS.from_formula # All of the lower case models accept ``formula`` and ``data`` arguments, # whereas upper case ones take ``endog`` and ``exog`` design matrices. # ``formula`` accepts a string which describes the model in terms of a -# ``patsy`` formula. ``data`` takes a [pandas](http://pandas.pydata.org/) +# ``patsy`` formula. ``data`` takes a [pandas](https://pandas.pydata.org/) # data frame or any other data structure that defines a ``__getitem__`` for # variable names like a structured array or a dictionary of variables. # diff --git a/examples/python/generic_mle.py b/examples/python/generic_mle.py index d6f55d0..cf34059 100644 --- a/examples/python/generic_mle.py +++ b/examples/python/generic_mle.py @@ -157,7 +157,7 @@ class NBin(GenericLikelihoodModel): # dataset is hosted in CSV format at the [Rdatasets repository](https://ra # w.githubusercontent.com/vincentarelbundock/Rdatasets). We use the # ``read_csv`` -# function from the [Pandas library](http://pandas.pydata.org) to load the +# function from the [Pandas library](https://pandas.pydata.org) to load the # data # in memory. We then print the first few columns: # diff --git a/statsmodels/distributions/edgeworth.py b/statsmodels/distributions/edgeworth.py index fb548d1..4eb0391 100644 --- a/statsmodels/distributions/edgeworth.py +++ b/statsmodels/distributions/edgeworth.py @@ -148,7 +148,7 @@ class ExpandedNormal(rv_continuous): specification of distributions, Revue de l'Institut Internat. de Statistique. 5: 307 (1938), reprinted in R.A. Fisher, Contributions to Mathematical Statistics. Wiley, 1950. - .. [*] http://en.wikipedia.org/wiki/Edgeworth_series + .. [*] https://en.wikipedia.org/wiki/Edgeworth_series .. [*] S. Blinnikov and R. Moessner, Expansions for nearly Gaussian distributions, Astron. Astrophys. Suppl. Ser. 130, 193 (1998) diff --git a/statsmodels/examples/ex_outliers_influence.py b/statsmodels/examples/ex_outliers_influence.py index 91d10a4..34035db 100644 --- a/statsmodels/examples/ex_outliers_influence.py +++ b/statsmodels/examples/ex_outliers_influence.py @@ -39,7 +39,7 @@ if __name__ == '__main__': res = res_ols #alias - #http://en.wikipedia.org/wiki/PRESS_statistic + #https://en.wikipedia.org/wiki/PRESS_statistic #predicted residuals, leave one out predicted residuals resid_press = res.resid / (1-hh) ess_press = np.dot(resid_press, resid_press) @@ -47,7 +47,7 @@ if __name__ == '__main__': sigma2_est = np.sqrt(res.mse_resid) #can be replace by different estimators of sigma sigma_est = np.sqrt(sigma2_est) resid_studentized = res.resid / sigma_est / np.sqrt(1 - hh) - #http://en.wikipedia.org/wiki/DFFITS: + #https://en.wikipedia.org/wiki/DFFITS: dffits = resid_studentized * np.sqrt(hh / (1 - hh)) nobs, k_vars = res.model.exog.shape @@ -56,7 +56,7 @@ if __name__ == '__main__': res_ols.df_modelwc = res_ols.df_model + 1 n_params = res.model.exog.shape[1] - #http://en.wikipedia.org/wiki/Cook%27s_distance + #https://en.wikipedia.org/wiki/Cook%27s_distance cooks_d = res.resid**2 / sigma2_est / res_ols.df_modelwc * hh / (1 - hh)**2 #or #Eubank p.93, 94 diff --git a/statsmodels/graphics/functional.py b/statsmodels/graphics/functional.py index dcb45f3..c209818 100644 --- a/statsmodels/graphics/functional.py +++ b/statsmodels/graphics/functional.py @@ -452,7 +452,7 @@ def hdrboxplot(data, ncomp=2, alpha=None, threshold=0.95, bw=None, handles, labels = ax.get_legend_handles_labels() # Proxy artist for fill_between legend entry - # See http://matplotlib.org/1.3.1/users/legend_guide.html + # See https://matplotlib.org/1.3.1/users/legend_guide.html plt = _import_mpl() for label, fill_between in zip(['50% HDR', '90% HDR'], fill_betweens): p = plt.Rectangle((0, 0), 1, 1, diff --git a/statsmodels/graphics/plot_grids.py b/statsmodels/graphics/plot_grids.py index 14e480b..3ede11b 100644 --- a/statsmodels/graphics/plot_grids.py +++ b/statsmodels/graphics/plot_grids.py @@ -4,7 +4,7 @@ Author: Josef Perktold License: BSD-3 TODO: update script to use sharex, sharey, and visible=False - see http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label + see https://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label for sharex I need to have the ax of the last_row when editing the earlier rows. Or you axes_grid1, imagegrid http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html diff --git a/statsmodels/nonparametric/kde.py b/statsmodels/nonparametric/kde.py index f29e3c1..87cb258 100644 --- a/statsmodels/nonparametric/kde.py +++ b/statsmodels/nonparametric/kde.py @@ -7,7 +7,7 @@ Racine, Jeff. (2008) "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88. http://dx.doi.org/10.1561/0800000009 -http://en.wikipedia.org/wiki/Kernel_%28statistics%29 +https://en.wikipedia.org/wiki/Kernel_%28statistics%29 Silverman, B.W. Density Estimation for Statistics and Data Analysis. """ diff --git a/statsmodels/nonparametric/kernel_density.py b/statsmodels/nonparametric/kernel_density.py index cd5ce2f..ceed04f 100644 --- a/statsmodels/nonparametric/kernel_density.py +++ b/statsmodels/nonparametric/kernel_density.py @@ -214,7 +214,7 @@ class KDEMultivariate(GenericKDE): Notes ----- - See http://en.wikipedia.org/wiki/Cumulative_distribution_function + See https://en.wikipedia.org/wiki/Cumulative_distribution_function For more details on the estimation see Ref. [5] in module docstring. The multivariate CDF for mixed data (continuous and ordered/unordered @@ -392,7 +392,7 @@ class KDEMultivariateConditional(GenericKDE): References ---------- - .. [1] http://en.wikipedia.org/wiki/Conditional_probability_distribution + .. [1] https://en.wikipedia.org/wiki/Conditional_probability_distribution Examples -------- diff --git a/statsmodels/regression/linear_model.py b/statsmodels/regression/linear_model.py index 90c6b08..11f45bc 100644 --- a/statsmodels/regression/linear_model.py +++ b/statsmodels/regression/linear_model.py @@ -1283,7 +1283,7 @@ def yule_walker(X, order=1, method="unbiased", df=None, inv=False, See, for example: - http://en.wikipedia.org/wiki/Autoregressive_moving_average_model + https://en.wikipedia.org/wiki/Autoregressive_moving_average_model Parameters ---------- diff --git a/statsmodels/regression/recursive_ls.py b/statsmodels/regression/recursive_ls.py index 363d6d1..982859f 100644 --- a/statsmodels/regression/recursive_ls.py +++ b/statsmodels/regression/recursive_ls.py @@ -615,7 +615,7 @@ class RecursiveLSResults(MLEResults): # Only add CI to legend for the first plot if i == 0: # Proxy artist for fill_between legend entry - # See http://matplotlib.org/1.3.1/users/legend_guide.html + # See https://matplotlib.org/1.3.1/users/legend_guide.html p = plt.Rectangle((0, 0), 1, 1, fc=ci_poly.get_facecolor()[0]) diff --git a/statsmodels/sandbox/distributions/extras.py b/statsmodels/sandbox/distributions/extras.py index ec702cc..58f2a1e 100644 --- a/statsmodels/sandbox/distributions/extras.py +++ b/statsmodels/sandbox/distributions/extras.py @@ -302,7 +302,7 @@ def pdf_mvsk(mvsk): References ---------- - http://en.wikipedia.org/wiki/Edgeworth_series + https://en.wikipedia.org/wiki/Edgeworth_series Johnson N.L., S. Kotz, N. Balakrishnan: Continuous Univariate Distributions, Volume 1, 2nd ed., p.30 """ @@ -350,7 +350,7 @@ def pdf_moments(cnt): References ---------- - http://en.wikipedia.org/wiki/Edgeworth_series + https://en.wikipedia.org/wiki/Edgeworth_series Johnson N.L., S. Kotz, N. Balakrishnan: Continuous Univariate Distributions, Volume 1, 2nd ed., p.30 """ diff --git a/statsmodels/sandbox/distributions/otherdist.py b/statsmodels/sandbox/distributions/otherdist.py index 1b06bf1..52569a0 100644 --- a/statsmodels/sandbox/distributions/otherdist.py +++ b/statsmodels/sandbox/distributions/otherdist.py @@ -8,7 +8,7 @@ Author: Josef Perktold Notes: Compound Poisson has mass point at zero -http://en.wikipedia.org/wiki/Compound_Poisson_distribution +https://en.wikipedia.org/wiki/Compound_Poisson_distribution and would need special treatment need a distribution that has discrete mass points and contiuous range, e.g. diff --git a/statsmodels/sandbox/distributions/sppatch.py b/statsmodels/sandbox/distributions/sppatch.py index b6abe66..0d4b9ea 100644 --- a/statsmodels/sandbox/distributions/sppatch.py +++ b/statsmodels/sandbox/distributions/sppatch.py @@ -78,7 +78,7 @@ def _fitstart_beta(self, x, fixed=None): References ---------- for method of moment estimator for known loc and scale - http://en.wikipedia.org/wiki/Beta_distribution#Parameter_estimation + https://en.wikipedia.org/wiki/Beta_distribution#Parameter_estimation http://www.itl.nist.gov/div898/handbook/eda/section3/eda366h.htm NIST reference also includes reference to MLE in Johnson, Kotz, and Balakrishan, Volume II, pages 221-235 @@ -140,7 +140,7 @@ def _fitstart_poisson(self, x, fixed=None): References ---------- MLE : - http://en.wikipedia.org/wiki/Poisson_distribution#Maximum_likelihood + https://en.wikipedia.org/wiki/Poisson_distribution#Maximum_likelihood ''' #todo: separate out this part to be used for other compact support distributions diff --git a/statsmodels/sandbox/regression/kernridgeregress_class.py b/statsmodels/sandbox/regression/kernridgeregress_class.py index fae28f0..aad33d4 100644 --- a/statsmodels/sandbox/regression/kernridgeregress_class.py +++ b/statsmodels/sandbox/regression/kernridgeregress_class.py @@ -33,7 +33,7 @@ class GaussProcess(object): * automatic selection or proposal of smoothing parameters Note: this is different from kernel smoothing regression, - see for example http://en.wikipedia.org/wiki/Kernel_smoother + see for example https://en.wikipedia.org/wiki/Kernel_smoother In this version of the kernel ridge regression, the training points are fitted exactly. diff --git a/statsmodels/sandbox/stats/diagnostic.py b/statsmodels/sandbox/stats/diagnostic.py index 3ee099d..cbd0a34 100644 --- a/statsmodels/sandbox/stats/diagnostic.py +++ b/statsmodels/sandbox/stats/diagnostic.py @@ -584,7 +584,7 @@ def het_breuschpagan(resid, exog_het): References ---------- - http://en.wikipedia.org/wiki/Breusch%E2%80%93Pagan_test + https://en.wikipedia.org/wiki/Breusch%E2%80%93Pagan_test Greene 5th edition Breusch, Pagan article diff --git a/statsmodels/sandbox/stats/runs.py b/statsmodels/sandbox/stats/runs.py index 21d2155..5087468 100644 --- a/statsmodels/sandbox/stats/runs.py +++ b/statsmodels/sandbox/stats/runs.py @@ -469,7 +469,7 @@ def cochrans_q(x): References ---------- - http://en.wikipedia.org/wiki/Cochran_test + https://en.wikipedia.org/wiki/Cochran_test SAS Manual for NPAR TESTS ''' diff --git a/statsmodels/sandbox/tsa/varma.py b/statsmodels/sandbox/tsa/varma.py index 5b22f2c..d3da463 100644 --- a/statsmodels/sandbox/tsa/varma.py +++ b/statsmodels/sandbox/tsa/varma.py @@ -60,8 +60,8 @@ def VAR(x,B, const=0): References ---------- - http://en.wikipedia.org/wiki/Vector_Autoregression - http://en.wikipedia.org/wiki/General_matrix_notation_of_a_VAR(p) + https://en.wikipedia.org/wiki/Vector_Autoregression + https://en.wikipedia.org/wiki/General_matrix_notation_of_a_VAR(p) ''' p = B.shape[0] T = x.shape[0] diff --git a/statsmodels/stats/contingency_tables.py b/statsmodels/stats/contingency_tables.py index 9438c51..38ad16f 100644 --- a/statsmodels/stats/contingency_tables.py +++ b/statsmodels/stats/contingency_tables.py @@ -1383,7 +1383,7 @@ def cochrans_q(x, return_object=True): References ---------- - http://en.wikipedia.org/wiki/Cochran_test + https://en.wikipedia.org/wiki/Cochran_test SAS Manual for NPAR TESTS """ diff --git a/statsmodels/stats/inter_rater.py b/statsmodels/stats/inter_rater.py index e4b03a5..36726bb 100644 --- a/statsmodels/stats/inter_rater.py +++ b/statsmodels/stats/inter_rater.py @@ -18,8 +18,8 @@ License: BSD-3 References ---------- Wikipedia: kappa's initially based on these two pages - http://en.wikipedia.org/wiki/Fleiss%27_kappa - http://en.wikipedia.org/wiki/Cohen's_kappa + https://en.wikipedia.org/wiki/Fleiss%27_kappa + https://en.wikipedia.org/wiki/Cohen's_kappa SAS-Manual : formulas for cohens_kappa, especially variances see also R package irr @@ -226,7 +226,7 @@ def fleiss_kappa(table, method='fleiss'): References ---------- - Wikipedia http://en.wikipedia.org/wiki/Fleiss%27_kappa + Wikipedia https://en.wikipedia.org/wiki/Fleiss%27_kappa Fleiss, Joseph L. 1971. "Measuring Nominal Scale Agreement among Many Raters." Psychological Bulletin 76 (5): 378-82. diff --git a/statsmodels/stats/moment_helpers.py b/statsmodels/stats/moment_helpers.py index 4bbfeb4..a65dd33 100644 --- a/statsmodels/stats/moment_helpers.py +++ b/statsmodels/stats/moment_helpers.py @@ -114,7 +114,7 @@ def mnc2cum(mnc): """convert non-central moments to cumulants recursive formula produces as many cumulants as moments - http://en.wikipedia.org/wiki/Cumulant#Cumulants_and_moments + https://en.wikipedia.org/wiki/Cumulant#Cumulants_and_moments """ X = _convert_to_multidim(mnc) diff --git a/statsmodels/stats/outliers_influence.py b/statsmodels/stats/outliers_influence.py index b33ab93..088cc03 100644 --- a/statsmodels/stats/outliers_influence.py +++ b/statsmodels/stats/outliers_influence.py @@ -122,7 +122,7 @@ def reset_ramsey(res, degree=5): References ---------- - http://en.wikipedia.org/wiki/Ramsey_RESET_test + https://en.wikipedia.org/wiki/Ramsey_RESET_test """ order = degree + 1 @@ -174,7 +174,7 @@ def variance_inflation_factor(exog, exog_idx): References ---------- - http://en.wikipedia.org/wiki/Variance_inflation_factor + https://en.wikipedia.org/wiki/Variance_inflation_factor """ k_vars = exog.shape[1] @@ -748,7 +748,7 @@ class OLSInfluence(_BaseInfluenceMixin): References ---------- - `Wikipedia `_ + `Wikipedia `_ """ # TODO: do I want to use different sigma estimate in diff --git a/statsmodels/stats/proportion.py b/statsmodels/stats/proportion.py index 3270828..9d45501 100644 --- a/statsmodels/stats/proportion.py +++ b/statsmodels/stats/proportion.py @@ -67,7 +67,7 @@ def proportion_confint(count, nobs, alpha=0.05, method='normal'): References ---------- - http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval + https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval Brown, Lawrence D.; Cai, T. Tony; DasGupta, Anirban (2001). "Interval Estimation for a Binomial Proportion", diff --git a/statsmodels/tools/eval_measures.py b/statsmodels/tools/eval_measures.py index 45abeee..01c6ff3 100644 --- a/statsmodels/tools/eval_measures.py +++ b/statsmodels/tools/eval_measures.py @@ -330,7 +330,7 @@ def aic(llf, nobs, df_modelwc): References ---------- - http://en.wikipedia.org/wiki/Akaike_information_criterion + https://en.wikipedia.org/wiki/Akaike_information_criterion """ return -2. * llf + 2. * df_modelwc @@ -355,7 +355,7 @@ def aicc(llf, nobs, df_modelwc): References ---------- - http://en.wikipedia.org/wiki/Akaike_information_criterion#AICc + https://en.wikipedia.org/wiki/Akaike_information_criterion#AICc """ return -2. * llf + 2. * df_modelwc * nobs / (nobs - df_modelwc - 1.) @@ -380,7 +380,7 @@ def bic(llf, nobs, df_modelwc): References ---------- - http://en.wikipedia.org/wiki/Bayesian_information_criterion + https://en.wikipedia.org/wiki/Bayesian_information_criterion """ return -2. * llf + np.log(nobs) * df_modelwc @@ -462,7 +462,7 @@ def aic_sigma(sigma2, nobs, df_modelwc, islog=False): References ---------- - http://en.wikipedia.org/wiki/Akaike_information_criterion + https://en.wikipedia.org/wiki/Akaike_information_criterion """ if not islog: @@ -497,7 +497,7 @@ def aicc_sigma(sigma2, nobs, df_modelwc, islog=False): References ---------- - http://en.wikipedia.org/wiki/Akaike_information_criterion#AICc + https://en.wikipedia.org/wiki/Akaike_information_criterion#AICc """ if not islog: @@ -532,7 +532,7 @@ def bic_sigma(sigma2, nobs, df_modelwc, islog=False): References ---------- - http://en.wikipedia.org/wiki/Bayesian_information_criterion + https://en.wikipedia.org/wiki/Bayesian_information_criterion """ if not islog: diff --git a/statsmodels/tools/numdiff.py b/statsmodels/tools/numdiff.py index e913b57..23ef7e3 100644 --- a/statsmodels/tools/numdiff.py +++ b/statsmodels/tools/numdiff.py @@ -36,13 +36,13 @@ without dependencies. # also does it hold only at the minimum, what's relationship to covariance # of Jacobian matrix # http://projects.scipy.org/scipy/ticket/1157 -# http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm +# https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm # objective: sum((y-f(beta,x)**2), Jacobian = d f/d beta # and not d objective/d beta as in MLE Greene # similar: http://crsouza.blogspot.com/2009/11/neural-network-learning-by-levenberg_18.html#hessian # # in example: if J = d x*beta / d beta then J'J == X'X -# similar to http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm +# similar to https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm from __future__ import print_function from statsmodels.compat.python import range import numpy as np diff --git a/statsmodels/tsa/statespace/structural.py b/statsmodels/tsa/statespace/structural.py index 3121e20..d806340 100644 --- a/statsmodels/tsa/statespace/structural.py +++ b/statsmodels/tsa/statespace/structural.py @@ -1610,7 +1610,7 @@ class UnobservedComponentsResults(MLEResults): ci_label = '$%.3g \\%%$ confidence interval' % ((1 - alpha) * 100) # Proxy artist for fill_between legend entry - # See e.g. http://matplotlib.org/1.3.1/users/legend_guide.html + # See e.g. https://matplotlib.org/1.3.1/users/legend_guide.html p = plt.Rectangle((0, 0), 1, 1, fc=ci_poly.get_facecolor()[0]) # Legend diff --git a/statsmodels/tsa/stattools.py b/statsmodels/tsa/stattools.py index 9cffdcd..95fbb73 100644 --- a/statsmodels/tsa/stattools.py +++ b/statsmodels/tsa/stattools.py @@ -1228,7 +1228,7 @@ def grangercausalitytests(x, maxlag, addconst=True, verbose=True): References ---------- - http://en.wikipedia.org/wiki/Granger_causality + https://en.wikipedia.org/wiki/Granger_causality Greene: Econometric Analysis """ diff --git a/tools/R2nparray/DESCRIPTION b/tools/R2nparray/DESCRIPTION index 40e2a56..c3ebb64 100644 --- a/tools/R2nparray/DESCRIPTION +++ b/tools/R2nparray/DESCRIPTION @@ -6,7 +6,7 @@ Author: Skipper Seabold Maintainer: Skipper Seabold Description: Writes R matrices, vectors, and scalars to a file as numpy arrays License: BSD -URL: http://www.github.com/statsmodels/statsmodels +URL: https://www.github.com/statsmodels/statsmodels Repository: github Collate: 'R2nparray-package.R' diff --git a/tools/matplotlibrc.qt4 b/tools/matplotlibrc.qt4 index 7e9bda2..83783d9 100644 --- a/tools/matplotlibrc.qt4 +++ b/tools/matplotlibrc.qt4 @@ -126,7 +126,7 @@ font.monospace : Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, #text.color : black -### LaTeX customizations. See http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex +### LaTeX customizations. See https://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex #text.usetex : False # use latex for all text handling. The following fonts # are supported through the usual rc parameter settings: # new century schoolbook, bookman, times, palatino, @@ -200,7 +200,7 @@ axes.axisbelow : True # whether axis gridlines and ticks are below # of the axis range is smaller than the # first or larger than the second #axes.unicode_minus : True # use unicode for the minus symbol - # rather than hypen. See http://en.wikipedia.org/wiki/Plus_sign#Plus_sign + # rather than hypen. See https://en.wikipedia.org/wiki/Plus_sign#Plus_sign axes.color_cycle : 348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33 # E24A33 : orange # 7A68A6 : purple diff --git a/tools/matplotlibrc.qt5 b/tools/matplotlibrc.qt5 index 396b6a6..0e7d2a2 100644 --- a/tools/matplotlibrc.qt5 +++ b/tools/matplotlibrc.qt5 @@ -12,7 +12,7 @@ # other platforms: # $HOME/.matplotlib/matplotlibrc # -# See http://matplotlib.org/users/customizing.html#the-matplotlibrc-file for +# See https://matplotlib.org/users/customizing.html#the-matplotlibrc-file for # more details on the paths which are checked for the configuration file. # # This file is best viewed in a editor which supports python mode @@ -76,7 +76,7 @@ backend.qt5 : PyQt5 ### LINES -# See http://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more +# See https://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more # information on line properties. #lines.linewidth : 1.0 # line width in points #lines.linestyle : - # solid line @@ -95,7 +95,7 @@ backend.qt5 : PyQt5 ### PATCHES # Patches are graphical objects that fill 2D space, like polygons or # circles. See -# http://matplotlib.org/api/artist_api.html#module-matplotlib.patches +# https://matplotlib.org/api/artist_api.html#module-matplotlib.patches # information on patch properties #patch.linewidth : 1.0 # edge width in points #patch.facecolor : blue @@ -105,7 +105,7 @@ backend.qt5 : PyQt5 ### FONT # # font properties used by text.Text. See -# http://matplotlib.org/api/font_manager_api.html for more +# https://matplotlib.org/api/font_manager_api.html for more # information on font properties. The 6 font properties used for font # matching are given below with their default values. # @@ -157,7 +157,7 @@ backend.qt5 : PyQt5 ### TEXT # text properties used by text.Text. See -# http://matplotlib.org/api/artist_api.html#module-matplotlib.text for more +# https://matplotlib.org/api/artist_api.html#module-matplotlib.text for more # information on text properties #text.color : black @@ -235,7 +235,7 @@ backend.qt5 : PyQt5 ### AXES # default face and edge color, default tick sizes, # default fontsizes for ticklabels, and so on. See -# http://matplotlib.org/api/axes_api.html#module-matplotlib.axes +# https://matplotlib.org/api/axes_api.html#module-matplotlib.axes #axes.hold : True # whether to clear the axes by default on #axes.facecolor : white # axes background color #axes.edgecolor : black # axes edge color @@ -266,7 +266,7 @@ backend.qt5 : PyQt5 #axes.unicode_minus : True # use unicode for the minus symbol # rather than hyphen. See - # http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes + # https://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes #axes.prop_cycle : cycler('color', 'bgrcmyk') # color cycle for plot lines # as list of string colorspecs: @@ -279,7 +279,7 @@ backend.qt5 : PyQt5 #axes3d.grid : True # display grid on 3d axes ### TICKS -# see http://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick +# see https://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick #xtick.major.size : 4 # major tick size in points #xtick.minor.size : 2 # minor tick size in points #xtick.major.width : 0.5 # major tick width in points @@ -328,7 +328,7 @@ backend.qt5 : PyQt5 #legend.scatterpoints : 3 # number of scatter points ### FIGURE -# See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure +# See https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure #figure.titlesize : medium # size of the figure title #figure.titleweight : normal # weight of the figure title #figure.figsize : 8, 6 # figure size in inches diff --git a/tools/notebook_output_template.py b/tools/notebook_output_template.py index b4d303a..d188713 100644 --- a/tools/notebook_output_template.py +++ b/tools/notebook_output_template.py @@ -9,7 +9,7 @@ notebook_template = Template(""" $body - +