aplr

v10.23.0 safe
3.0
Low Risk

Automatic Piecewise Linear Regression

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activities such as network calls, shell executions, or credential harvesting. The metadata risk is slightly elevated due to the maintainer having only one package.

  • No network calls
  • Single package from maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The maintainer has only one package, suggesting a potentially new or less active account.

📦 Package Quality Overall: Low (4.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. tests.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 32 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in ottenbreit-data-science/aplr
  • Two distinct contributors found

🔬 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: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ottenbreit-data-science/aplr appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Mathias von Ottenbreit" 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 aplr 
Develop a data analysis tool using Python that helps users understand complex time-series data through piecewise linear regression. This tool will leverage the 'aplr' package to automatically segment and analyze data into multiple linear segments, providing insights into different phases of growth, decline, or stability within the dataset.

The application should have the following functionalities:
1. Data Import: Users should be able to upload their own time-series datasets (CSV format).
2. Data Visualization: Initially, display the raw data in a line chart to give users an overview of the dataset.
3. Piecewise Linear Regression Analysis: Utilize the 'aplr' package to perform automatic piecewise linear regression on the imported data. The application should identify breakpoints where the slope changes significantly.
4. Visualization of Segments: Display each identified segment as a separate line on the chart, highlighting the start and end points of each segment.
5. Statistical Summary: Provide a summary table showing key statistics for each segment, such as start/end times, slopes, and R-squared values.
6. Interactive Features: Allow users to zoom in/out and pan across the chart to explore specific segments more closely.
7. Export Results: Enable users to export the analysis results (both visual and statistical summaries) to CSV or PDF formats.

To utilize the 'aplr' package effectively, focus on its ability to automatically detect breakpoints without manual intervention. Ensure that the application can handle noisy data and provide meaningful segmentation even when the data trends are not perfectly linear.

This project aims to democratize advanced analytics techniques like piecewise linear regression, making them accessible to non-experts in data science and statistics.

💬 Discussion Feed

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