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rclsp: A Modular Two-Step Convex Optimization Estimator for Ill-Posed Problems

Convex Least Squares Programming (CLSP) is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction methods inspired by Lasso, Ridge, and Elastic Net to ensure numerical stability, constraint enforcement, and interpretability. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R^2, normalized RMSE (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands.

Version: 0.1.0
Depends: R (≥ 4.2)
Imports: Matrix, stats, methods, CVXR, MASS
Suggests: testthat (≥ 3.0.0)
Published: 2025-11-17
DOI: 10.32614/CRAN.package.rclsp (may not be active yet)
Author: Ilya Bolotov ORCID iD [aut, cre]
Maintainer: Ilya Bolotov <ilya.bolotov at vse.cz>
BugReports: https://github.com/econcz/rclsp/issues
License: MIT + file LICENSE
URL: https://github.com/econcz/rclsp
NeedsCompilation: no
Language: en-US
Materials: README
CRAN checks: rclsp results

Documentation:

Reference manual: rclsp.html , rclsp.pdf

Downloads:

Package source: rclsp_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): rclsp_0.1.0.tgz, r-oldrel (x86_64): rclsp_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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