added 'method' argument to 'posthoc_wilcoxon' method (fixes #67), int…

…roduced a lower limit for scipy, discarded support for python 3.7, 3.8

.github/workflows/package-test.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.12"]
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}

pyproject.toml

@@ -7,7 +7,7 @@ name = "scikit-posthocs"
 dynamic = ["version"]
 description = "Statistical post-hoc analysis and outlier detection algorithms"
 readme = "DESCRIPTION.rst"
-requires-python = ">=3.6"
+requires-python = ">=3.9"
 keywords = ["statistics", "stats", "posthoc", "anova", "data science"]
 license.file = "LICENSE"
 authors = [
@@ -22,9 +22,6 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Mathematics",
     "License :: OSI Approved :: MIT License",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.6",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
@@ -34,7 +31,7 @@ urls.homepage = "https://github.com/maximtrp/scikit-posthocs"
 urls.documentation = "https://scikit-posthocs.rtfd.io"
 dependencies = [
     "numpy",
-    "scipy",
+    "scipy>=1.9.0",
     "statsmodels",
     "pandas>=0.20.0",
     "seaborn",

scikit_posthocs/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.8.2'
+__version__ = '0.9.0'
 
 from scikit_posthocs._global import global_simes_test, global_f_test
 from scikit_posthocs._omnibus import test_osrt, test_durbin, test_mackwolfe

scikit_posthocs/_posthocs.py

@@ -1566,7 +1566,7 @@ def posthoc_dunnett(a: Union[list, np.ndarray, DataFrame],
                     group_col: str = None,
                     control: str = None,
                     sort: bool = False,
-                    to_matrix: bool = True) -> Series | DataFrame:
+                    to_matrix: bool = True) -> Union[Series, DataFrame]:
     """
     Dunnett's test [1, 2, 3] for multiple comparisons against a control group, used after parametric
     ANOVA. The control group is specified by the `control` parameter.
@@ -1590,6 +1590,7 @@ def posthoc_dunnett(a: Union[list, np.ndarray, DataFrame],
     control : str, optional
         Name of the control group within the `group_col` column. Values should
         have a nominal scale (categorical). Must be specified if `a` is a pandas
+        DataFrame.
 
     sort : bool, optional
         Specifies whether to sort DataFrame by group_col or not. Recommended
@@ -1914,6 +1915,7 @@ def posthoc_wilcoxon(
         a: Union[list, np.ndarray, DataFrame],
         val_col: str = None,
         group_col: str = None,
+        method: str = 'auto',
         zero_method: str = 'wilcox',
         correction: bool = False,
         p_adjust: str = None,
@@ -1939,7 +1941,10 @@ def posthoc_wilcoxon(
         (grouping or predictor variable). Values should have a nominal scale
         (categorical). Must be specified if `a` is a pandas DataFrame object.
 
-    zero_method : string, {"pratt", "wilcox", "zsplit"}, optional
+    method : {"auto", "exact", "approx"}, optional
+        Method to calculate the p-value. Default is "auto".
+
+    zero_method : {"pratt", "wilcox", "zsplit"}, optional
         "pratt": Pratt treatment, includes zero-differences in the ranking
         process (more conservative)
         "wilcox": Wilcox treatment, discards all zero-differences
@@ -1977,7 +1982,11 @@ def posthoc_wilcoxon(
 
     Notes
     -----
-    Refer to `scipy.stats.wilcoxon` reference page for further details.
+    Refer to `scipy.stats.wilcoxon` reference page for further details [1]_.
+
+    References
+    ----------
+    .. [1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html
 
     Examples
     --------
@@ -2002,6 +2011,7 @@ def posthoc_wilcoxon(
             xg.get_group(groups[i]),
             xg.get_group(groups[j]),
             zero_method=zero_method,
+            method=method,
             correction=correction)[1]
 
     if p_adjust: