Metadata-Version: 2.1
Name: mlr-x
Version: 1.0.1
Summary: Multiple linear regression for low- and high-dimensional data, including validation, diagnostics, applicability domain analysis, visualization, and predictive modeling.
Author-email: "Jackson J. Alcázar" <jjalcazar.dev@gmail.com>
License: AGPL-3.0-only
Project-URL: Homepage, https://jacksonalcazar.github.io/MLR-X
Project-URL: Repository, https://github.com/Jacksonalcazar/MLR-X
Project-URL: Issues, https://github.com/Jacksonalcazar/MLR-X/issues
Project-URL: Donate, https://www.paypal.com/donate/?hosted_button_id=TTWN9EKMWAHFG
Keywords: multiple linear regression,model selection,statistical analysis,variable selection,heuristic search,high-dimensional data,qsar,applicability domain,linear prediction,data science,data mining
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: statsmodels
Requires-Dist: pillow
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: joblib

<img src="https://raw.githubusercontent.com/Jacksonalcazar/MLR-X/html/icon.png" width="100" align="left" style="margin-right:1px;"/>

<br>

<b>MLR-X</b> is a cross-platform software package for multiple linear regression designed for low- and high-dimensional data, integrating model fitting, subset selection, validation, diagnostic analysis, and prediction into a unified workflow.
<br>

The software implements a reproducible heuristic search strategy (EPR-S: Expand–Perturb–Reduce–Swap recovery) to explore the model space while enforcing statistical constraints such as significance thresholds, multicollinearity control, and correlation filtering.

MLR-X provides a comprehensive set of internal and external validation metrics, applicability-domain assessment, and graphical diagnostics, enabling rigorous model evaluation and interpretation. Results are automatically compiled into structured, export-ready reports suitable for research and publication.

<p align="center">
  <img src="https://raw.githubusercontent.com/Jacksonalcazar/MLR-X/html/figure.png" width="700"/>
</p>

## Install

```bash
pip install mlr-x
```

## Run

Launch GUI mode:

```bash
mlrx
```

Run CLI mode:

```bash
mlrx <config.conf> 
```

Or

```bash
mlrx <config.conf> [--onlyIV]
```

Helpful parameters:

- `--version`: show the version and exit.
- `--model`: select a model identifier for requested outputs.
- `--outputs`: define which outputs to generate (for example: `diagnostics`, `visualization`, `summary`).
- `pdf`, `png`, `tiff`, and `svg` are export formats used for visualization outputs.
- `--noruns`: use an existing results file from the configuration output path.
- `--onlyIV` and `--onlyEV`: execute internal or external validation only, respectively, using models from an existing results file at the configured output path. Both options skip model search and require that the results file already exists.

Example:

```bash
python MLRX.py example.conf --model 1 --outputs summary
```
## Requirements

- Python 3.10+

On Linux, install GUI dependencies if needed:

```bash
sudo apt-get install python3-tk
sudo apt-get install xvfb
```

## Prebuilt binaries

You can also download standalone binaries from the official release:

- <https://github.com/Jacksonalcazar/MLR-X/releases/tag/v1.0>

Available platforms:

- Windows 10/11 (64-bit)
- macOS X (Arm64)
- Ubuntu 20.04 (x86-64)

## How to cite

Article under review. For now, please cite as follows:

- Alcázar, Jackson J. (2026). "MLR-X 1.0 software. Available at: https://jacksonalcazar.github.io/MLR-X/".
