robustHD: Robust Methods for High-Dimensional Linear Model Selection

robustHD is an R package implementing robust methods for high-dimensional data, authored by Andreas Alfons. The package specifically provides robust least angle regression, robust groupwise least angle regression, and sparse least trimmed squares regression techniques. The methods are based on published research from 2007, 2013, and 2016.

Use Cases

• Performing robust variable selection in high-dimensional linear models based on least angle regression.
• Applying groupwise variable selection with robustness to outliers based on the described groupwise least angle regression method.
• Fitting sparse regression models using a robust least trimmed squares criterion as mentioned in the description.

Strengths

• Implements peer-reviewed methods from three specific research papers (2007, 2013, 2016).
• Focuses on robust techniques designed for high-dimensional data analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Andreas Alfons

Collection Method

Software package implementing published statistical algorithms.