A Modular Two-Step Convex Optimization Estimator for Ill-Posed Problems R package rclsp version 2.0.1 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. Software Depends: R (>= 4.3) Imports: Matrix, stats, methods, CVXR (>= 1.8.1) Suggests: testthat (>= 3.0.0) Ilya Bolotov <ilya.bolotov@vse.cz> Comprehensive R Archive Network (CRAN) Ilya Bolotov [aut, cre] (ORCID: <https://orcid.org/0000-0003-1148-7144>) MIT + file LICENSE (https://CRAN.R-project.org/package=rclsp/LICENSE) 2026-06-11 application/tgz https://CRAN.R-project.org/package=rclsp doi:10.32614/CRAN.package.rclsp en-US