Merge pull request #8866 from bashtage/doc-admonitions

DOC: Add admonitions for changes and deprecations

docs/source/_static/stylesheets/deprecation.css

@@ -23,7 +23,7 @@ div.deprecated, div.versionadded, div.versionchanged {
     position: relative;
     transition: color .25s,background-color .25s,border-color .25s;
     vertical-align: middle;
-    font-size: 0.64rem;
+    font-size: 0.72rem;
 }
 div.deprecated>p, div.versionadded>p, div.versionchanged>p {
     margin-bottom: .6rem;
@@ -35,6 +35,11 @@ div.deprecated {
     background-color: rgba(255, 0, 0, 0.1);
 }
 
+div.versionadded {
+    border-color: #459db9;
+    background-color: rgba(0, 191, 255, 0.1);
+}
+
 span.versionmodified {
     font-weight: 600;
 }
@@ -47,7 +52,14 @@ div.deprecated p:before {
     background-color: #dc3545;
 }
 
+div.versionadded p:before {
+    background-color: #459db9;
+}
+
 span.versionmodified.deprecated:before {
     color: #dc3545;
 }
 
+span.versionmodified.added {
+    color: #459db9;
+}

docs/source/release/index.rst

@@ -7,7 +7,7 @@ Release Notes
 .. toctree::
    :maxdepth: 1
 
-   version0.14
+   version0.14.0
    version0.13.4-5
    version0.13.3
    version0.13.2

docs/source/release/version0.14.rst → docs/source/release/version0.14.0.rst

@@ -21,11 +21,11 @@ was already in statsmodels main and how much usage it has already
 seen.  If there are specific known problems or limitations, then they
 are mentioned in the docstrings.
 
-Github Stats
-------------
-**Issues Closed**: 248
+Stats
+-----
+**Issues Closed**: 255
 
-**Pull Requests Merged**: 335
+**Pull Requests Merged**: 345
 
 
 The Highlights
@@ -44,14 +44,14 @@ selection or treatment model, outcome model and effect functions.
 
 Hurdle and Truncated Count Regression
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-:class:`statsmodels.discrete.truncated_model.HurdleModel` implements
+:class:`statsmodels.discrete.truncated_model.HurdleCountModel` implements
 hurdle models for count data with either Poisson or NegativeBinomialP as
 submodels.
 Three left truncated models used for zero truncation are available,
 :class:`statsmodels.discrete.truncated_model.TruncatedLFPoisson`,
 :class:`statsmodels.discrete.truncated_model.TruncatedLFNegativeBinomialP`
 and
-:class:`statsmodels.discrete.truncated_model.TruncatedLFGeneralized Poisson`.
+:class:`statsmodels.discrete.truncated_model.TruncatedLFGeneralizedPoisson`.
 Models for right censoring at one are implemented but only as support for
 the hurdle models.
 
@@ -108,7 +108,7 @@ Other Notable Enhancments
 -------------------------
 
 - burg option in pacf :pr:`8113`
-- new link for GLM: logc :pr:`8155`
+- new link for GLM: Logc :pr:`8155`
 - rename class names for links for GLM, lower case names are deprecated :pr:`8569`
 - allow singular covariance in gaussian copula :pr:`8504`
 - GLM: Tweedie full loglikelihood :pr:`8560`
@@ -144,7 +144,7 @@ Submodules
 - Fix a typo in the documentation  (:pr:`8275`)
 - Fix `histogram`  (:pr:`8299`)
 - Add notebook for Poisson post-estimation overview  (:pr:`8420`)
-
+- Add version  (:pr:`8863`)
 
 
 
@@ -186,6 +186,7 @@ Submodules
 - MNLogit if endog is series with no name   (:pr:`8674`)
 - Get_distribution, return 1-d instead of column frozen distribution  (:pr:`8780`)
 - Numpy compat, indexed assignment shape in NegativeBinomial  (:pr:`8822`)
+- Support offset in truncated count models  (:pr:`8845`)
 
 
 
@@ -245,6 +246,9 @@ Submodules
 - Update PRs in release note  (:pr:`8805`)
 - Improve release notes highlights  (:pr:`8806`)
 - Fix more deprecations and restore doc build  (:pr:`8826`)
+- Final changes for 0.14.0rc0 notes  (:pr:`8839`)
+- Fix internet address of dataset  (:pr:`8861`)
+- Small additional fixes  (:pr:`8862`)
 
 
 
@@ -616,7 +620,7 @@ Thanks to all of the contributors for the 0.14.0 release (based on git log):
 - Eva Maxfield Brown
 - Evgeny Zhurko
 - Ewout Ter Hoeven
-- Geoffrey M. Oxberry
+- Geoffrey Oxberry
 - Greg Mcmahan
 - Gregory Parkes
 - Guilherme Silveira
@@ -629,6 +633,7 @@ Thanks to all of the contributors for the 0.14.0 release (based on git log):
 - Josef Perktold
 - João Tanaka
 - Kees Mulder
+- Kerby Shedden
 - Kevin Sheppard
 - Kirill Milash
 - Kirill Ulanov
@@ -1019,3 +1024,14 @@ The following Pull Requests were merged since the last release:
 - :pr:`8831`: BUG: Correct initial level, treand and seasonal
 - :pr:`8834`: MAINT: Switch DeprecationWarn to FutureWarn
 - :pr:`8835`: ENH: Add sharex for seasonal decompose plots
+- :pr:`8839`: DOC: Final changes for 0.14.0rc0 notes
+- :pr:`8845`: BUG/ENH: support offset in truncated count models
+- :pr:`8847`: DOC: Use JSON for versioning
+- :pr:`8851`: BUG: Fix added variable plots to work with OLS
+- :pr:`8857`: DOC: Small fix for STLForecast example
+- :pr:`8858`: DOC: Fix example notebooks
+- :pr:`8861`: DOC: Fix internet address of dataset
+- :pr:`8862`: DOC: Small additional fixes
+- :pr:`8863`: DOC: Add version
+- :pr:`8865`: MAINT: Move from Styler.applymap to map
+- :pr:`8866`: DOC: Add admonitions for changes and deprecations

statsmodels/discrete/discrete_model.py

@@ -506,6 +506,11 @@ def predict(self, params, exog=None, which="mean", linear=None,
             - 'var' returns the estimated variance of endog implied by the
               model.
 
+            .. versionadded: 0.14
+
+               ``which`` replaces and extends the deprecated ``linear``
+               argument.
+
         linear : bool
             If True, returns the linear predicted values.  If False or None,
             then the statistic specified by ``which`` will be returned.
@@ -741,9 +746,28 @@ def predict(self, params, exog=None, which="mean", linear=None):
             it assumed to be 1 row of exogenous variables. If you only have
             one regressor and would like to do prediction, you must provide
             a 2d array with shape[1] == 1.
-        linear : bool, optional
-            If True, returns the linear predictor dot(exog,params).  Else,
-            returns the value of the cdf at the linear predictor.
+        which : {'mean', 'linear', 'var', 'prob'}, optional
+            Statistic to predict. Default is 'mean'.
+
+            - 'mean' returns the conditional expectation of endog E(y | x),
+              i.e. exp of linear predictor.
+            - 'linear' returns the linear predictor of the mean function.
+            - 'var' returns the estimated variance of endog implied by the
+              model.
+
+            .. versionadded: 0.14
+
+               ``which`` replaces and extends the deprecated ``linear``
+               argument.
+
+        linear : bool
+            If True, returns the linear predicted values.  If False or None,
+            then the statistic specified by ``which`` will be returned.
+
+            .. deprecated: 0.14
+
+               The ``linear` keyword is deprecated and will be removed,
+               use ``which`` keyword instead.
 
         Notes
         -----
@@ -999,12 +1023,21 @@ def predict(self, params, exog=None, exposure=None, offset=None,
             - 'var' variance of endog implied by the likelihood model
             - 'prob' predicted probabilities for counts.
 
+            .. versionadded: 0.14
+
+               ``which`` replaces and extends the deprecated ``linear``
+               argument.
+
         linear : bool
-            The ``linear` keyword is deprecated and will be removed,
-            use ``which`` keyword instead.
             If True, returns the linear predicted values.  If False or None,
             then the statistic specified by ``which`` will be returned.
 
+            .. deprecated: 0.14
+
+               The ``linear` keyword is deprecated and will be removed,
+               use ``which`` keyword instead.
+
+
         Notes
         -----
         If exposure is specified, then it will be logged by the method.
@@ -1621,11 +1654,22 @@ def predict(self, params, exog=None, exposure=None, offset=None,
             - 'prob' return probabilities for counts from 0 to max(endog) or
               for y_values if those are provided.
 
+            .. versionadded: 0.14
+
+               ``which`` replaces and extends the deprecated ``linear``
+               argument.
+
         linear : bool
             The ``linear` keyword is deprecated and will be removed,
             use ``which`` keyword instead.
             If True, returns the linear predicted values.  If False or None,
             then the statistic specified by ``which`` will be returned.
+
+            .. deprecated: 0.14
+
+               The ``linear` keyword is deprecated and will be removed,
+               use ``which`` keyword instead.
+
         y_values : array_like
             Values of the random variable endog at which pmf is evaluated.
             Only used if ``which="prob"``

statsmodels/discrete/truncated_model.py

@@ -32,6 +32,8 @@ class TruncatedLFGeneric(CountModel):
     __doc__ = """
     Generic Truncated model for count data
 
+    .. versionadded:: 0.14.0
+
     %(params)s
     %(extra_params)s
 
@@ -433,6 +435,8 @@ class TruncatedLFPoisson(TruncatedLFGeneric):
     __doc__ = """
     Truncated Poisson model for count data
 
+    .. versionadded:: 0.14.0
+
     %(params)s
     %(extra_params)s
 
@@ -504,6 +508,8 @@ class TruncatedLFNegativeBinomialP(TruncatedLFGeneric):
     __doc__ = """
     Truncated Generalized Negative Binomial model for count data
 
+    .. versionadded:: 0.14.0
+
     %(params)s
     %(extra_params)s
 
@@ -588,6 +594,8 @@ class TruncatedLFGeneralizedPoisson(TruncatedLFGeneric):
     __doc__ = """
     Truncated Generalized Poisson model for count data
 
+    .. versionadded:: 0.14.0
+
     %(params)s
     %(extra_params)s
 
@@ -1038,6 +1046,8 @@ class HurdleCountModel(CountModel):
     __doc__ = """
     Hurdle model for count data
 
+    .. versionadded:: 0.14.0
+
     %(params)s
     %(extra_params)s
 

statsmodels/stats/_diagnostic_other.py

@@ -202,7 +202,10 @@ def dispersion_poisson(results):
        Each test has two strings a descriptive name and a string for the
        alternative hypothesis.
     """
-    msg = 'dispersion_poisson here is deprecated, use '
+    msg = (
+        'dispersion_poisson here is deprecated, use the version in '
+        'discrete._diagnostic_count'
+    )
     warnings.warn(msg, FutureWarning)
 
     from statsmodels.discrete._diagnostics_count import test_poisson_dispersion
@@ -225,7 +228,10 @@ def dispersion_poisson_generic(results, exog_new_test, exog_new_control=None,
 
     Warning: insufficiently tested, especially for options
     """
-    msg = 'dispersion_poisson here is deprecated, use '
+    msg = (
+        'dispersion_poisson_generic here is deprecated, use the version in '
+        'discrete._diagnostic_count'
+    )
     warnings.warn(msg, FutureWarning)
 
     from statsmodels.discrete._diagnostics_count import (

statsmodels/stats/multitest.py

@@ -400,13 +400,21 @@ def fdrcorrection_twostage(pvals, alpha=0.05, method='bky',
         maxiter=False is two-stage fdr (maxiter=1)
         maxiter=True is full iteration (maxiter=-1 or maxiter=len(pvals))
 
+        .. versionadded:: 0.14
+
+            Replacement for ``iter`` with additional features.
+
     iter : bool
         ``iter`` is deprecated use ``maxiter`` instead.
         If iter is True, then only one iteration step is used, this is the
         two-step method.
         If iter is False, then iterations are stopped at convergence which
         occurs in a finite number of steps (at most len(pvals) steps).
 
+        .. deprecated:: 0.14
+
+            Use ``maxiter`` instead of ``iter``.
+
     Returns
     -------
     rejected : ndarray, bool