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 [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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