An R interface provides access to the Lawson-Hanson implementation of the non-negative least squares algorithm. The package also allows for combining non-negative and non-positive constraints. It was authored by Katharine M. Mullen.
Use Cases
- Implementing constrained regression models based on the non-negative least squares algorithm.
- Solving optimization problems with positivity constraints using the Lawson-Hanson method.
- Combining non-negative and non-positive constraints in a single model as described in the package.
- Benchmarking NNLS algorithm performance against other implementations.
- Teaching the principles and application of the Lawson-Hanson NNLS algorithm.
Strengths
- Provides a direct interface to a well-known Lawson-Hanson algorithm implementation.
- Extends the core algorithm to allow combined non-negative and non-positive constraints.
Limitations
- Row count and dataset scale are unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Katharine M. Mullen