Merge pull request #63 from mdhaber/gh17569

DOC: 1.10 release notes updates for scipy.stats

doc/release/1.10.0-notes.rst

@@ -182,52 +182,59 @@ New features
   Cramer-von Mises, and Anderson-Darling).
 - Improved `scipy.stats.bootstrap`: Default method ``'BCa'`` now supports
   multi-sample statistics. Also, the bootstrap distribution is returned in the
-  result object, and the result object can be passed into the function to add
-  additional resamples or change the confidence interval level and type.
-- Added a ``confidence_interval`` method to the result object returned by
-  `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel``.
+  result object, and the result object can be passed into the function as
+  parameter ``bootstrap_result`` to add additional resamples or change the
+  confidence interval level and type.
 - Added maximum spacing estimation to `scipy.stats.fit`.
-- Added `scipy.stats.contingency.odds_ratio` to compute both the conditional and
-  unconditional odds ratio for 2x2 contingency tables and corresponding
-  confidence intervals.
 - Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`.
-- Added `scipy.stats.expectile`, which generalizes the expected value in the
-  same way as quantiles are a generalization of the median.
+- Added new sample statistics.
+
+  - Added `scipy.stats.contingency.odds_ratio` to compute both the conditional
+    and unconditional odds ratios and corresponding confidence intervals for
+    2x2 contingency tables.
+  - Added `scipy.stats.directional_stats` to compute sample statistics of
+    n-dimensional directional data.
+  - Added `scipy.stats.expectile`, which generalizes the expected value in the
+    same way as quantiles are a generalization of the median.
+
 - Added new statistical distributions.
 
   - Added `scipy.stats.uniform_direction`, a multivariate distribution to
     sample uniformly from the surface of a hypersphere.
   - Added `scipy.stats.random_table`, a multivariate distribution to sample
     uniformly from m x n contingency tables with provided marginals.
   - Added `scipy.stats.truncpareto`, the truncated Pareto distribution.
+
 - Improved the ``fit`` method of several distributions.
 
   - `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical
-    solution when ``method='mm'``, which also serves a starting guess to improve
-    the performance of ``method='mle'``.
+    solution when ``method='mm'``, which also serves a starting guess to
+    improve the performance of ``method='mle'``.
   - `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum
     likelihood estimates have been extended to the cases in which location or
     scale are fixed by the user.
   - Analytical maximum likelihood estimates have been added for
     `scipy.stats.powerlaw`.
+
 - Improved random variate sampling of several distributions.
 
   - Drawing multiple samples from `scipy.stats.matrix_normal`,
     `scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and
     `scipy.stats.unitary_group` is faster.
   - The ``rvs`` method of `scipy.stats.vonmises` now wraps to the interval
-    ``[-np.pi, np.pi]``
+    ``[-np.pi, np.pi]``.
   - Improved the reliability of `scipy.stats.loggamma` ``rvs`` method for small
     values of the shape parameter.
+
 - Improved the speed and/or accuracy of functions of several statistical
   distributions.
 
   - Added `scipy.stats.Covariance` for better speed, accuracy, and user control
     in multivariate normal calculations.
   - `scipy.stats.skewnorm` methods ``cdf``, ``sf``, ``ppf``, and ``isf``
     methods now use the implementations from Boost, improving speed while
-    maintaining accuracy. The calculation of higher-order moments is also faster
-    and more accurate.
+    maintaining accuracy. The calculation of higher-order moments is also
+    faster and more accurate.
   - `scipy.stats.invgauss` methods ``ppf`` and ``isf`` methods now use the
     implementations from Boost, improving speed and accuracy.
   - `scipy.stats.invweibull` methods ``sf`` and ``isf`` are more accurate for
@@ -244,7 +251,8 @@ New features
     allowing the user to change the integration limit from -inf to a desired
     value.
   - Improved the robustness of ``entropy`` of `scipy.stats.vonmises` for large
-    concentration values
+    concentration values.
+
 - Enhanced `scipy.stats.gaussian_kde`.
 
   - Added `scipy.stats.gaussian_kde.marginal`, which returns the desired
@@ -257,46 +265,53 @@ New features
     `scipy.stats.gaussian_kde` for improved multithreading performance.
   - Replaced explicit matrix inversion with Cholesky decomposition for speed
     and accuracy.
-- Statistical test functions that previously returned plain tuples now return
-  bunches, allowing attributes to be accessed by name. The `scipy.stats`
-  functions ``combine_pvalues``, ``fisher_exact``, ``chi2_contingency``,
-  ``median_test`` and ``mood`` have been updated.
-- The result objects returned by statistical test functions now consistently
-  use the attribute names ``statistic`` and ``pvalue``. Old attribute names are
-  allowed for backward compatibility. The `scipy.stats` functions
-  ``multiscale_graphcorr``, ``anderson_ksamp``, ``binomtest``, ``crosstab``,
-  ``pointbiserialr``, ``spearmanr``, ``kendalltau`` and ``weightedtau`` have
-  been updated.
-- `scipy.stats.anderson` now returns the parameters of the fitted distribution
-  in a `scipy.stats._result_classes.FitResult` object.
-- Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) return more information:
-  the location (argmax) at which the statistic is calculated, and the variant
-  of the statistic used.
-- Improved the performance of `scipy.stats.cramervonmises_2samp` and
-  `scipy.stats.ks_2samp` with ``method='exact'``.
-- Improved the performance of `scipy.stats.siegelslopes`.
-- Improved the performance of `scipy.stats.hdquantile_sd`
-- Added the ``scramble`` optional argument to `scipy.stats.qmc.LatinHypercube`. It
-  replaces ``centered`` which is now deprecated.
+
+- Enhanced the result objects returned by many `scipy.stats` functions
+
+  - Added a ``confidence_interval`` method to the result object returned by
+    `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`.
+  - The `scipy.stats` functions ``combine_pvalues``, ``fisher_exact``,
+    ``chi2_contingency``, ``median_test`` and ``mood`` now return
+    bunch objects rather than plain tuples, allowing attributes to be
+    accessed by name.
+  - Attributes of the result objects returned by ``multiscale_graphcorr``,
+    ``anderson_ksamp``, ``binomtest``, ``crosstab``, ``pointbiserialr``,
+    ``spearmanr``, ``kendalltau``, and ``weightedtau`` have been renamed to
+    ``statistic`` and ``pvalue`` for consistency throughout `scipy.stats`.
+    Old attribute names are still allowed for backward compatibility.
+  - `scipy.stats.anderson` now returns the parameters of the fitted
+    distribution in a `scipy.stats._result_classes.FitResult` object.
+  - The ``plot`` method of `scipy.stats._result_classes.FitResult` now accepts
+    a ``plot_type`` parameter; the options are ``'hist'`` (histogram, default),
+    ``'qq'`` (Q-Q plot), ``'pp'`` (P-P plot), and ``'cdf'`` (empirical CDF
+    plot).
+  - Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the
+    location (argmax) at which the statistic is calculated and the variant
+    of the statistic used.
+
+- Improved the performance of several `scipy.stats` functions.
+
+  - Improved the performance of `scipy.stats.cramervonmises_2samp` and
+    `scipy.stats.ks_2samp` with ``method='exact'``.
+  - Improved the performance of `scipy.stats.siegelslopes`.
+  - Improved the performance of `scipy.stats.hdquantile_sd`.
+  - Improved the performance of `scipy.stats.binned_statistic_dd` for several
+    NumPy statistics, and binned statistics methods now support complex data.
+
+- Added the ``scramble`` optional argument to `scipy.stats.qmc.LatinHypercube`.
+  It replaces ``centered``, which is now deprecated.
 - Added a parameter ``optimization`` to all `scipy.stats.qmc.QMCEngine`
-  subclasses to improve characteristics of the quasi-random variates
-- Added `scipy.stats.directional_stats` to compute sample statistics of
-  n-dimensional directional data.
-- The ``plot`` method of `scipy.stats._result_classes.FitResult` now accepts a
-  ``plot_type`` parameter; the options are ``'hist'`` (histogram, default),
-  ``'qq'`` (Q-Q plot), ``'pp'`` (P-P plot), and ``'cdf'`` (empirical CDF plot).
+  subclasses to improve characteristics of the quasi-random variates.
 - Added tie correction to `scipy.stats.mood`.
 - Added tutorials for resampling methods in `scipy.stats`.
 - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and
   `scipy.stats.monte_carlo_test` now automatically detect whether the provided
   ``statistic`` is vectorized, so passing the ``vectorized`` argument
   explicitly is no longer required to take advantage of vectorized statistics.
-- Improved speed of `scipy.stats.permutation_test` for permutation types
+- Improved the speed of `scipy.stats.permutation_test` for permutation types
   ``'samples'`` and ``'pairings'``.
-- Improved the performance of `scipy.stats.binned_statistic_dd` for several
-  NumPy statistics, and binned statistics methods now support complex data.
 - Added ``axis``, ``nan_policy``, and masked array support to
-  `scipy.stats.jarque_bera`
+  `scipy.stats.jarque_bera`.
 - Added the ``nan_policy`` optional argument to `scipy.stats.rankdata`.