Metadata-Version: 2.4
Name: acfmperiod
Version: 0.1.0
Summary: Robust and classical ACF/ACOVF estimation from periodogram and M-periodogram
Author: Higor Cotta, Valderio Reisen, Pascal Bondon, Celine Levy-Leduc
Maintainer-email: Higor Cotta <cotta.higor@gmail.com>
License: GPL-2.0-or-later
Project-URL: Homepage, https://github.com/rogih/acfmperiod-python
Project-URL: Repository, https://github.com/rogih/acfmperiod-python
Project-URL: Issues, https://github.com/rogih/acfmperiod-python/issues
Project-URL: Releases, https://github.com/rogih/acfmperiod-python/releases
Keywords: time-series,acf,periodogram,robust-statistics
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
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: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.22
Requires-Dist: matplotlib>=3.5
Requires-Dist: statsmodels>=0.14
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == "dev"
Provides-Extra: release
Requires-Dist: build>=1.2; extra == "release"
Requires-Dist: twine>=5.0; extra == "release"
Dynamic: license-file

# acfmperiod

Python port of the original R package `acfMPeriod` for classical and robust
autocovariance/autocorrelation estimation using periodogram and M-periodogram
methods.

## Features

- Univariate and multivariate ACF/ACOVF from periodogram (`PerACF`)
- Robust ACF/ACOVF from M-periodogram (`MPerACF`)
- Periodogram and cross-periodogram estimators
- Covariance/correlation matrix wrappers at lag 0 (`CovCorPer`, `CovCorMPer`)
- Plotting support for robust and classical outputs

## Installation

```bash
pip install acfmperiod
```

For local development:

```bash
pip install -e .[dev]
```

## Quick Start

```python
import numpy as np
from acfmperiod import PerACF, MPerACF, CovCorPer

rng = np.random.default_rng(7)
x = rng.normal(size=(128, 2))

res = PerACF(x, lagmax=20, typevalue="correlation", doplot=False)
print(res.acf.shape)   # (20, 2, 2)

rob = MPerACF(x, lagmax=20, doplot=False)
cov0 = CovCorPer(x, typevalue="covariance")
print(cov0)
```

## API

- `PerioReg`, `MPerioReg`
- `CrossPeriodogram`, `MCrossPeriodogram`
- `PerACF`, `MPerACF`
- `CovCorPer`, `CovCorMPer`
- `plotrobacf`

Lowercase aliases are available without underscore naming.
ß
