MAINT: stats: correct name Gilbrat -> Gibrat

doc/source/tutorial/stats.rst

@@ -100,7 +100,7 @@ introspection:
     >>> dist_discrete = [d for d in dir(stats) if
     ...                  isinstance(getattr(stats, d), stats.rv_discrete)]
     >>> print('number of continuous distributions: %d' % len(dist_continu))
-    number of continuous distributions: 105
+    number of continuous distributions: 106
     >>> print('number of discrete distributions:   %d' % len(dist_discrete))
     number of discrete distributions:   19
 

doc/source/tutorial/stats/continuous_gilbrat.rst

@@ -1,7 +1,7 @@
 
-.. _continuous-gilbrat:
+.. _continuous-gibrat:
 
-Gilbrat Distribution
+Gibrat Distribution
 ====================
 
 Special case of the log-normal with :math:`\sigma=1` and :math:`S=1.0`, typically also :math:`L=0.0`.)
@@ -27,4 +27,4 @@ Special case of the log-normal with :math:`\sigma=1` and :math:`S=1.0`, typicall
     \begin{eqnarray*} h\left[X\right] & = & \log\left(\sqrt{2\pi e}\right)\\
      & \approx & 1.4189385332046727418\end{eqnarray*}
 
-Implementation: `scipy.stats.gilbrat`
+Implementation: `scipy.stats.gibrat`

scipy/stats/__init__.py

@@ -85,7 +85,7 @@
    genhalflogistic   -- Generalized Half Logistic
    genhyperbolic     -- Generalized Hyperbolic
    geninvgauss       -- Generalized Inverse Gaussian
-   gilbrat           -- Gilbrat
+   gibrat            -- Gibrat
    gompertz          -- Gompertz (Truncated Gumbel)
    gumbel_r          -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
    gumbel_l          -- Left Sided Gumbel, etc.

scipy/stats/_continuous_distns.py

@@ -5425,20 +5425,20 @@ def fit(self, data, *args, **kwds):
 lognorm = lognorm_gen(a=0.0, name='lognorm')
 
 
-class gilbrat_gen(rv_continuous):
-    r"""A Gilbrat continuous random variable.
+class gibrat_gen(rv_continuous):
+    r"""A Gibrat continuous random variable.
 
     %(before_notes)s
 
     Notes
     -----
-    The probability density function for `gilbrat` is:
+    The probability density function for `gibrat` is:
 
     .. math::
 
         f(x) = \frac{1}{x \sqrt{2\pi}} \exp(-\frac{1}{2} (\log(x))^2)
 
-    `gilbrat` is a special case of `lognorm` with ``s=1``.
+    `gibrat` is a special case of `lognorm` with ``s=1``.
 
     %(after_notes)s
 
@@ -5478,7 +5478,8 @@ def _entropy(self):
         return 0.5 * np.log(2 * np.pi) + 0.5
 
 
-gilbrat = gilbrat_gen(a=0.0, name='gilbrat')
+gilbrat = gibrat_gen(a=0.0, name='gilbrat')
+gibrat = gibrat_gen(a=0.0, name='gibrat')
 
 
 class maxwell_gen(rv_continuous):

scipy/stats/_distr_params.py

@@ -45,6 +45,7 @@
     ['halfgennorm', (0.6748054997000371,)],
     ['genpareto', (0.1,)],   # use case with finite moments
     ['gilbrat', ()],
+    ['gibrat', ()],
     ['gompertz', (0.94743713075105251,)],
     ['gumbel_l', ()],
     ['gumbel_r', ()],
@@ -207,6 +208,7 @@
     ['genhalflogistic', (-1, )],
     ['geninvgauss', (1, 0)],
     ['gilbrat', ()],
+    ['gibrat', ()],
     ['gompertz', (-1, )],
     ['gumbel_r', ()],
     ['gumbel_l', ()],

scipy/stats/tests/test_distributions.py

@@ -171,7 +171,7 @@ def assert_fit_warnings(dist):
 @pytest.mark.parametrize('dist',
                          ['alpha', 'betaprime',
                           'fatiguelife', 'invgamma', 'invgauss', 'invweibull',
-                          'johnsonsb', 'levy', 'levy_l', 'lognorm', 'gilbrat',
+                          'johnsonsb', 'levy', 'levy_l', 'lognorm', 'gibrat',
                           'powerlognorm', 'rayleigh', 'wald'])
 def test_support(dist):
     """gh-6235"""

scipy/stats/tests/test_sampling.py

@@ -786,7 +786,7 @@ def test_basic(self, dist, mv_ex):
     # numerical differences. They can be avoided either by changing
     # the seed or by increasing the u_resolution.
     fail_dists = ['ncf', 'pareto', 'chi2', 'fatiguelife', 'halfgennorm',
-                  'gilbrat', 'lognorm', 'ncx2', 't']
+                  'gibrat', 'lognorm', 'ncx2', 't']
 
     @pytest.mark.xslow
     @pytest.mark.parametrize("distname, params", distcont)