rlma 0.2.0
Robust Linear Matching Algorithm (RLMA) for variable association detection
Requires Python: >=3.6

# rlma

**Robust Linear Matching Algorithm (RLMA)**

A Python package to detect and quantify relationships between variables using a novel linear matching algorithm that:

- Groups variables by categorical levels based on mean and standard deviation  
- Compares independent variables (IVs) and dependent variables (DVs) via category matching  
- Supports binary, continuous, and multivariate variables  
- Includes permutation testing for significance  
- Allows composite variable analysis via PCA, Ridge, or Random Forest predictions  

## Features

- Simple and intuitive method to detect relationships beyond classic linear correlation  
- Handles different types of variables: continuous, categorical, and binary  
- Permutation test to estimate the significance of the relationship  
- Batch processing for multiple IVs with ranking by relationship strength  
- Composite analysis to handle many IVs in high-dimensional data  

## Installation

Install the package using pip:

pip install rlma

## Usage Examples
import pandas as pd
from rlma import compute_RMI, permutation_test, batch_pairwise_rlma, composite_rlma

# Sample data for demonstration
data = {
    'burnout': [4.87, 4.83, 3.83, 4.31, 4.73, 1.41, 2.80, 4.50],
    'anxiety': [4.80, 4.55, 4.30, 4.30, 3.95, 1.25, 4.85, 4.00],
    'metacognition': [2.60, 2.50, 4.20, 4.16, 2.93, 1.76, 3.56, 4.90],
}

X = pd.DataFrame(data)
y = X['anxiety']  # For example, treat anxiety as dependent variable

### 1. Compute RLMA between one IV and DV
rmi_value = compute_RMI(X['burnout'], y)
print(f"RMI between burnout and anxiety: {rmi_value:.4f}")

### 2. Perform permutation test for significance
perm_results = permutation_test(X['burnout'], y, n_perm=2000)
print(f"Permutation test p-value: {perm_results['emp_p']:.4f}")

### 3. Run batch RLMA on all IVs against anxiety
batch_results = batch_pairwise_rlma(X, y, n_perm=1000)
print("Batch RLMA results:")
print(batch_results)

### 4. Composite RLMA using ridge regression to combine IVs predicting anxiety
comp_result = composite_rlma(X, y, method='ridge', n_perm=1000)
print(f"Composite RMI: {comp_result['rmi_result']:.4f}")
print(f"Composite permutation p-value: {comp_result['perm_result']['emp_p']:.4f}")
API Reference
compute_RMI(X, y, num_categories=3, alpha=0.5)
Calculate the Robust Linear Matching Index between variables X and y.

X, y: pandas Series or numpy arrays

num_categories: Number of categories to divide data into (default 3)
alpha: Weight for near-matches (default 0.5)
Returns a float between 0 and 1 indicating the strength of the relationship.

permutation_test(X, y, n_perm=1000)
Perform a permutation test to assess the significance of the RLMA relationship.
n_perm: Number of permutations (default 1000)

Returns a dictionary with empirical p-value under 'emp_p'.

batch_pairwise_rlma(X_df, y, n_perm=1000)
Apply RLMA to all columns of X_df against y, including permutation testing.

Returns a pandas DataFrame ranking IVs by RLMA strength and significance.

composite_rlma(X_df, y, method='ridge', n_perm=1000)
Generate a composite IV prediction using PCA, Ridge, or Random Forest, then compute RLMA.

method: One of 'pca', 'ridge', 'rf' (default 'ridge')

Returns a dict with RLMA value and permutation test result.

## Contributing
Contributions, issues, and feature requests are welcome! Please open an issue or pull request.

### License
This project is licensed under the MIT License.

# Author
Francis Jemisiham Amofa
Email: jemisihamamofa@gmail.com
GitHub: https://github.com/francisamofa