🤖 AI Analysis

Final verdict: SAFE

Overall, the package EmaCalc v1.1.7 is considered safe as it lacks significant indicators of malicious activity. However, the metadata risk score of 5 out of 10 warrants caution due to the absence of maintainer history and a GitHub repository.

No network calls or shell executions detected.
Lack of maintainer history and associated GitHub repository increases metadata risk.

Per-check LLM notes

Network: No network calls detected, which is normal for a basic calculation package.
Shell: No shell execution detected, indicating no risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The package shows some red flags due to lack of maintainer history and no associated GitHub repo, but there's not enough evidence to conclude it's malicious.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: kth.se>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with EmaCalc

Create a fully-functional mini-application named 'EMAAnalyzer' using the Python package 'EmaCalc'. This application aims to simplify the statistical analysis of Ecological Momentary Assessment (EMA) data for researchers and practitioners in behavioral science. EMA is a method used to collect real-time data about people's behaviors, experiences, and environments in their natural settings.

The application should include the following core functionalities:
1. **Data Importation**: Users should be able to import EMA data from CSV files or directly from a database. The data typically includes timestamps, participant IDs, and various measurements (e.g., mood, activity level).
2. **Basic Descriptive Statistics**: Calculate and display basic statistics such as mean, median, mode, standard deviation, and variance for each measurement type across different time periods.
3. **Advanced Statistical Analysis**: Utilize 'EmaCalc' to perform more complex analyses like correlation coefficients between different measurements, regression models to predict outcomes based on predictors, and time-series analysis to understand trends over time.
4. **Visualization**: Provide visual representations of the data through graphs and charts. Include options for line plots to show trends over time, scatter plots for correlations, and histograms for distributions.
5. **Customizable Reports**: Allow users to generate customizable reports summarizing the analysis results. These reports should be exportable in PDF format.
6. **User Interface**: Develop a user-friendly interface using a framework like PyQt or Tkinter. The UI should guide users through the process of importing data, selecting analysis types, viewing results, and generating reports.
7. **Error Handling and Validation**: Implement robust error handling to manage issues like invalid file formats, missing data, and incorrect input values.
8. **Documentation**: Provide comprehensive documentation explaining how to use the application, including examples of how to analyze specific datasets.

To achieve these goals, you will need to utilize the 'EmaCalc' package effectively. For instance, when performing advanced statistical analyses, make sure to leverage 'EmaCalc's functions for calculating correlation coefficients and regression models. Additionally, consider how 'EmaCalc' can assist in preparing data for visualization and report generation.

This project not only serves as a practical tool for analyzing EMA data but also showcases the capabilities of 'EmaCalc' in handling complex statistical tasks.