fix descriptions in readme.md

CHANGELOG.md

@@ -1,3 +1,11 @@
+## 0.7.4 (2025-07-04)
+
+### Fix
+
+- allow string input for expression parameter in U_stats_loop function
+- improve formatting and readability of backend documentation in _backend.py
+- update citation section in README.md to be commented out and adjust version reference format in pyproject.toml
+
 ## 0.7.3 (2025-07-04)
 
 ### Fix

README.md

@@ -8,7 +8,7 @@
 
 This package provides a high-performance, tensor-based implementation for computing U-statistics and V-statistics with significant computational advantages:
 
-- Leverages the underlying combinatorial structure of kernel functions to significantly reduce computational complexity from exponential to polynomial in many cases
+- Leverages the underlying combinatorial structure of kernel functions to significantly reduce computational complexity in many cases
 - Utilizes optimized einsum engines—[`numpy.einsum`](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html) and [`torch.einsum`](https://pytorch.org/docs/stable/generated/torch.einsum.html)—to enable efficient computation on both CPU and GPU
 
 ## Table of Contents
@@ -28,6 +28,7 @@ Install the package from PyPI:
 ```bash
 pip install u-stats
 ```
+
 For development installation:
 
 ```bash
@@ -96,9 +97,9 @@ $$
 
 The expression defines how kernel matrices are connected in the computation. We take this U-statistic as an example to explain how to construct expression. 
 
-to express the structure of the kernel function $h(x_1, x_2, \dots, x_7) = h_1(x_1, x_2) \cdot h_2(x_2, x_3) \cdots h_{6}(x_{6}, x_7)$, we assign a unique index to each distinct variable $x_1, x_2, \dots, x_7$. For each factor $h_k(x_{k}, x_{k+1})$, we collect the indices of the variables it depends on into a pair. The sequence of pairs is then ordered according to the order of the factors in the product. 
+To express the structure of the kernel function $h(x_1, x_2, \dots, x_7) = h_1(x_1, x_2) \cdot h_2(x_2, x_3) \cdots h_{6}(x_{6}, x_7)$, we assign a unique index to each distinct variable $x_1, x_2, \dots, x_7$. For each factor $h_k(x_{k}, x_{k+1})$, we collect the indices of the variables it depends on into a pair. The sequence of pairs is then ordered according to the order of the factors in the product. 
 
-We can using the following notation to represent this structure: 
+We can use the following notation to represent this structure: 
 
 **Einstein Summation Notation:**
 ```python
@@ -113,7 +114,7 @@ expression = [[1,2],[2,3],[3,4],[4,5],[5,6],[6,7]]
 **Format Explanation:**
 - In Einstein notation: each letter represents an index, each string like `"ab"` represents a factor of the kernel
 - In list notation: each sub-list `[i,j]` represents a factor of the kernel
-Both formats are equivalent and specify the same computation pattern.
+Both formats are equivalent and specify the same computation pattern in our package
 
 #### 3.2.3 Complete Example
 
@@ -281,6 +282,5 @@ For questions or discussions, feel free to open an issue or reach out to the mai
   url={https://github.com/Amedar-Asterisk/U-Statistics-python},
   version={0.6.1},
   year={2024}
-
 }
 ``` -->

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "u-stats"
-version = "0.7.3"
+version = "0.7.4"
 authors = [
     {name = "Ruiqi Zhang", email = "zrq1706@outlook.com"}
 ]
@@ -106,7 +106,7 @@ ignore_missing_imports = true
 name = "cz_conventional_commits"
 tag_format = "$version"
 version_scheme = "semver"
-version = "0.7.3"
+version = "0.7.4"
 version_files = [
     "pyproject.toml:version",
     "src/u_stats/__init__.py:__version__",

src/u_stats/__init__.py

@@ -25,7 +25,7 @@
 """
 
 __title__ = "u_stats"
-__version__ = "0.7.3"
+__version__ = "0.7.4"
 __description__ = "A Python package for efficient computation of U-statistics via tensor contraction."  # noqa: E501
 __author__ = "Zhang Ruiqi"
 __author_email__ = "zrq1706@outlook.com"