Metadata-Version: 2.4
Name: spssmirror
Version: 2.1.0
Summary: A unified, self-contained statistical analysis library for Python -- an SPSS/R replacement.
Home-page: https://github.com/yourusername/spssmirror
Author: SPSSMirror Contributors
Author-email: you@example.com
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5.0
Requires-Dist: numpy>=1.23.0
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Requires-Dist: statsmodels>=0.14.0
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Requires-Dist: patsy>=0.5.3
Requires-Dist: rapidfuzz>=2.0.0
Requires-Dist: factor_analyzer>=0.4.0
Provides-Extra: bayesian
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Requires-Dist: arviz>=0.15.0; extra == "bayesian"
Provides-Extra: timeseries
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Provides-Extra: survival
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Provides-Extra: multivariate
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Dynamic: author
Dynamic: author-email
Dynamic: classifier
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Dynamic: home-page
Dynamic: license-file
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# SPSSMirror

A unified, self-contained statistical analysis library for Python -- an SPSS/R replacement.

## What's new in 2.1.0

- **Full pandas passthrough**: any method not defined by SPSSMirror falls through
  automatically to the underlying pandas DataFrame -- `mirror.head()`, `mirror.dtypes`,
  `mirror.groupby(...)`, `mirror.fillna(...)`, all of pandas, completely unmodified.
- **Flat API**: every statistical method now lives directly on `SPSSMirror`
  (`mirror.t_test_independent(...)`, `mirror.linear_regression(...)`,
  `mirror.cronbach_alpha(...)`) -- no more `.regression().linear(...)` nesting required
  (the old grouped accessors like `.regression()` still work too, unchanged).
- **Pandas-style output**: every result renders as a real pandas Series or DataFrame
  (`print(result)` gives a clean aligned table; bare `result` in Jupyter renders as a grid).
- **`.hint()`**: `mirror.hint()` lists every available statistical method as a
  DataFrame; `mirror.hint("regression")` filters to one group.

```python
from spssmirror import SPSSMirror

mirror = SPSSMirror().load_csv("survey.csv")
mirror.head()                                   # normal pandas
result = mirror.linear_regression("score ~ age + C(group)")
print(result)                                    # pandas-style table
mirror.hint("regression")                        # see what else is available
```

## Installation

```bash
pip install spssmirror
pip install spssmirror[bayesian,timeseries,survival,multivariate]  # optional extras
```

## License

MIT
