Release 4.1.0

HISTORY.rst

@@ -2,9 +2,15 @@
 History
 =======
 
-in development
---------------
-* Modernize infrastructure (pyproject.toml, uv, ruff, update dependencies)
+4.1.0 (2026-02-26)
+------------------
+* Modernize build infrastructure: migrate to pyproject.toml, remove setup.py/setup.cfg/Makefile
+* Switch to ruff for linting and formatting, add ty for type checking
+* Use uv and tox-uv for faster CI and local development
+* Add type annotations throughout the codebase (Optional parameters, return types, abstract methods)
+* Remove dead code: unused functions, constants, and commented-out class stubs
+* Fix pandas deprecation warnings
+* Remove BetaBinomial.as_cumulative() classmethod (unused, untested)
 
 4.0.0 (2024-11-24)
 ------------------

README.md

@@ -2,7 +2,7 @@ Spotify Confidence
 ========
 
 ![Status](https://img.shields.io/badge/Status-Beta-blue.svg)
-![Latest release](https://img.shields.io/badge/release-4.0.0-green.svg "Latest release: 4.0.0")
+![Latest release](https://img.shields.io/badge/release-4.1.0-green.svg "Latest release: 4.1.0")
 ![Python](https://img.shields.io/badge/Python-3.9-blue.svg "Python")
 ![Python](https://img.shields.io/badge/Python-3.10-blue.svg "Python")
 ![Python](https://img.shields.io/badge/Python-3.11-blue.svg "Python")
@@ -12,15 +12,15 @@ Python library for AB test analysis.
 Why use Spotify Confidence?
 -----------------
 
-Spotify Confidence provides convenience wrappers around statsmodel's various functions for computing p-values and confidence intervalls. 
-With Spotify Confidence it's easy to compute several p-values and confidence bounds in one go, e.g. one for each country or for each date. 
-Each function comes in two versions: 
+Spotify Confidence provides convenience wrappers around statsmodel's various functions for computing p-values and confidence intervalls.
+With Spotify Confidence it's easy to compute several p-values and confidence bounds in one go, e.g. one for each country or for each date.
+Each function comes in two versions:
  - one that return a pandas dataframe,
  - one that returns a [Chartify](https://github.com/spotify/chartify) chart.
 
-Spotify Confidence has support calculating p-values and confidence intervals using Z-statistics, Student's T-statistics 
-(or more exactly [Welch's T-test](https://en.wikipedia.org/wiki/Welch%27s_t-test)), as well as Chi-squared statistics. 
-It also supports a variance reduction technique based on using pre-exposure data to fit a linear model.  
+Spotify Confidence has support calculating p-values and confidence intervals using Z-statistics, Student's T-statistics
+(or more exactly [Welch's T-test](https://en.wikipedia.org/wiki/Welch%27s_t-test)), as well as Chi-squared statistics.
+It also supports a variance reduction technique based on using pre-exposure data to fit a linear model.
 
 There is also a Bayesian alternative in the BetaBinomial class.
 
@@ -44,7 +44,7 @@ test = confidence.ZTest(
     denominator_column='total',
     categorical_group_columns='variation_name',
     correction_method='bonferroni')
-    
+
 test.summary()
 test.difference(level_1='control', level_2='treatment1')
 test.multiple_difference(level='control', level_as_reference=True)
@@ -74,4 +74,3 @@ Spotify Confidence can be installed via pip:
 ### Code of Conduct
 
 This project adheres to the [Open Code of Conduct](https://github.com/spotify/code-of-conduct/blob/master/code-of-conduct.md) By participating, you are expected to honor this code.
-

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotify-confidence"
-version = "4.0.0"
+version = "4.1.0"
 description = "Package for calculating and visualising confidence intervals, e.g. for A/B test analysis."
 readme = "README.md"
 license = {file = "LICENSE"}