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Merge pull request #99 from Digiratory/55-discussion-update-readme-to-clarify-installation-steps
55 update readme and documentation to clarify installation steps
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‎README.md‎

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<p align="center">An open-source application designed to facilitate feature dependency modeling and evaluation using Bayesian Networks.</p>
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</p>
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## User Guide
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You can obtain user guides for BN Modeller application with the following link https://digiratory.github.io/bayes_model/. It covers various aspects of using BN Modeller, including data analysis workflows, best practices, and more.
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## Instalation
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### From sources
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You can install this project from sources by cloning this repository and installing it using pip:
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```bash
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git clone https://github.com/Digiratory/bayes_model.git && cd bayes_model
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pip install .
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```
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### Using PyPI
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You can install this project using pip:
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```bash
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pip install bn_modeller
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```
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or can download the latest Windows executable from the [BN Modeller GitHub Releases page](https://github.com/Digiratory/bayes_model/releases).
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### Using executable file (windows only)
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You can download the latest Windows executable from the [BN Modeller GitHub Releases page](https://github.com/Digiratory/bayes_model/releases).
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### Graphviz
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A Graphviz error could arise. To solve the problem add the Graphviz executables on your systems' PATH as follows:
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A Graphviz error could arise. To solve the problem install and add the Graphviz executables on your systems' PATH as follows:
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<details>
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<summary>
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Windows
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</summary>
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1. Install windows package from: <https://graphviz.org/download/> (Linux and Mac instructions can be found here as well)
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2. Install python graphviz package
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<https://pygraphviz.github.io/documentation/stable/install.html>
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</details>
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<details>
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<summary>
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Linux
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</summary>
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```bash
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sudo apt-get update && sudo apt-get install graphviz graphviz-dev
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```
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</details>
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## Launch Application
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To launch the application installed with `pip`, run:

‎docs/index.md‎

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---
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layout: default
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title: Bayesian Networks Modeler
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nav_order: 1
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---
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# Bayesian Networks Modeler
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* **Data Import:** BN Modeller supports the import of datasets from common file formats, including CSV (Comma Separated Values) and Excel spreadsheets. This allows you to easily incorporate your existing data into the application for analysis.
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* **Project-Based Data Management:** To streamline your research, BN Modeller utilizes a project-based system. This allows you to organize and manage multiple datasets within a single project, preserving the state of your work and facilitating easy switching between different analyses.
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* **Bayesian Network Construction via Correlation Coefficients:** BN Modeller allows you to build and visualize Bayesian Networks based on statistical correlation measures. Specifically, you can utilize Pearson's correlation coefficient (measuring linear relationships), Spearman's rank correlation coefficient (measuring monotonic relationships), or partial correlation coefficients (accounting for the influence of other variables). A brief explanation of correlation coefficients is provided on the page [Understanding Correlation](./understanding-correlation.html).
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* **Bayesian Network Construction via Correlation Coefficients:** BN Modeller allows you to build and visualize Bayesian Networks based on statistical correlation measures. Specifically, you can utilize Pearson's correlation coefficient (measuring linear relationships), Spearman's rank correlation coefficient (measuring monotonic relationships), or partial correlation coefficients (accounting for the influence of other variables). A brief explanation of correlation coefficients is provided on the page [Understanding Correlation](./user-guide/understanding-correlation.html).
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* **Correlation Evaluation:** The application provides tools to evaluate the strength and significance of correlations between variables. This allows you to identify potential dependencies and inform the structure of your Bayesian Network.
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* **Manual Dependency Specification:** While BN Modeller can suggest potential dependencies based on correlation analysis, you retain full control over the network structure. You can manually select and specify the dependencies between variables, allowing you to incorporate domain expertise and refine the model.
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* **Bayesian Network Visualization and Export:** BN Modeller allows you to export your constructed Bayesian Networks as image files in various formats, including PNG and SVG. This enables you to easily share your findings and incorporate the visualizations into reports and presentations.

‎docs/user-guide/data-analysis-workflow.md‎

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---
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title: Data Analysis Workflow
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parent: User Guide - Welcome!
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nav_order: 1
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---
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# Data Analysis Workflow
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After loading your data into BN Modeller, you can proceed with data analysis and model building. This section outlines the steps involved in this process.

‎docs/user-guide/index.md‎

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---
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layout: default
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title: User Guide - Welcome!
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nav_order: 2
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---
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# User Guide - Welcome!
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This User Guide provides comprehensive instructions and explanations for using BN Modeller. Whether you're a novice or an experienced Bayesian network modeler, this guide will help you leverage the software's features to build, analyze, and visualize your networks.

docs/understanding-correlation.md renamed to docs/user-guide/understanding-correlation.md

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title: Understanding Correlation Coefficients
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parent: User Guide - Welcome!
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nav_order: 2
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---
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# Understanding Correlation Coefficients
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BN Modeller utilizes several correlation coefficients to help you identify potential dependencies between variables when constructing Bayesian Networks. Here's a brief explanation of each:

‎docs/whatsnew.md‎

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---
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layout: default
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title: What's New?
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nav_order: 3
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---
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# What's New?
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## [Version 1.0.0](https://github.com/Digiratory/bayes_model/releases/tag/release%2F1.0.0) - Initial Release
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Date: 12 may 2025

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