🤖 AI Analysis

Final verdict: SAFE

The package appears safe based on the analysis. There are no indications of malicious activities, network risks, shell execution risks, obfuscation, or credential harvesting.

Low metadata risk due to a single package from the maintainer.
No detected network calls, shell executions, obfuscations, or credential harvesting.

Per-check LLM notes

Network: No network calls detected, which is normal for a package focused on local computations like OPLS-MD.
Shell: No shell execution patterns detected, aligning with the expected behavior of a scientific computation library.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

🔬 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: helsinki.fi

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository molecularmachinist/OPLS-MD appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author ""Santeri Paajanen, Shreyas Kaptan"" 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 OPLS-MD

Create a comprehensive data analysis tool using the OPLS-MD Python package. This tool will allow users to perform Partial Least Squares (PLS) and Orthogonal Projections to Latent Structures (OPLS) regression analyses on their datasets. The application should be designed to accept user input for dataset upload, feature selection, and model training. Additionally, it should provide visualizations of the results and key metrics to evaluate the performance of the models.

Steps to develop the application:
1. Set up a clean virtual environment and install necessary packages including OPLS-MD.
2. Design a user-friendly interface where users can upload their datasets in CSV format.
3. Implement a feature selection module that allows users to choose which columns they want to use as predictors and which as responses.
4. Integrate the OPLS-MD package to enable PLS and OPLS regression analysis based on user selections.
5. Develop a visualization component that displays the results of the analysis, such as score plots, loading plots, and VIP plots.
6. Include a performance evaluation section that provides metrics like R-squared, Q-squared, and cross-validation results.
7. Ensure the application is well-documented with instructions and explanations of the output.

Suggested Features:
- Support for multiple file formats (CSV, Excel).
- Option to handle missing values automatically or manually.
- Advanced options for customization of the regression models.
- Export functionality for reports and visualizations.
- Interactive tutorials for new users.

Utilizing the 'OPLS-MD' package, your application will leverage its powerful capabilities in multivariate analysis to provide deep insights into complex datasets.