🤖 AI Analysis

Final verdict: SUSPICIOUS

The package SUITPy v2.1.1 exhibits a potential typosquatting attempt targeting 'scipy', raising suspicion about its legitimacy despite other checks showing low risk.

Potential typosquatting targeting 'scipy'
No significant risks detected in network, shell, or obfuscation checks

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some red flags but not severe enough to conclusively indicate malice.
⚠ Typosquatting target: scipy

🔬 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 score 3.0

Possible typosquat of: scipy

"SUITPy" is 2 edit(s) from "scipy"

✓ Registered Email Domain

Email domain looks legitimate: googlemail.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://diedrichsenlab.org/imaging/suit.htm

✓ Git Repository History

Repository DiedrichsenLab/SUITPy appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Jorn Diedrichsen" 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 SUITPy

Your task is to create a Python-based mini-application that leverages the SUITPy package to analyze cerebellar fMRI data. This application will serve as a user-friendly tool for researchers and neuroscientists to preprocess, analyze, and visualize cerebellar fMRI datasets. Here’s a detailed breakdown of what your application should achieve:

1. **Data Importation**: Allow users to import their cerebellar fMRI data in standard formats such as NIfTI (.nii) files.
2. **Preprocessing Workflow**: Implement a series of preprocessing steps using SUITPy, including motion correction, slice timing correction, spatial normalization, and smoothing.
3. **Statistical Analysis**: Utilize SUITPy to perform statistical analyses on the preprocessed data, such as activation detection and group-level comparisons.
4. **Visualization**: Integrate visualization tools within the application to display the results of the analysis, such as activation maps and statistical plots.
5. **User Interface**: Develop a simple yet effective graphical user interface (GUI) using a library like PyQt or Tkinter, which allows users to interact with the application without needing to write code.
6. **Report Generation**: Enable the generation of comprehensive reports summarizing the analysis results, including statistical metrics, visualizations, and interpretations.
7. **Documentation and Help**: Provide thorough documentation and help resources for users to understand the functionality and usage of the application.

**How to Utilize SUITPy**:
- Use `suitpy.preprocessing` module for implementing preprocessing steps.
- Leverage `suitpy.stats` for conducting statistical analyses on the data.
- Employ `suitpy.visualization` for creating visual outputs of the analysis results.

This application aims to streamline the process of analyzing cerebellar fMRI data, making it accessible and efficient for both novice and experienced users.