Linear Programming via Regularized Least Squares R package rlppinv version 2.0.0 The Linear Programming via Regularized Least Squares (LPPinv) is a two-stage estimation method that reformulates linear programs as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, LPPinv solves linear inequality, equality, and bound constraints by (1) constructing a canonical constraint system and computing a pseudoinverse projection, followed by (2) a convex-programming correction stage to refine the solution under additional regularization (e.g., Lasso, Ridge, or Elastic Net). LPPinv is intended for underdetermined and ill-posed linear problems, for which standard solvers fail. Software Depends: R (>= 4.3) Imports: rclsp (>= 2.0.0) 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=rlppinv/LICENSE) 2026-06-11 application/tgz https://CRAN.R-project.org/package=rlppinv doi:10.32614/CRAN.package.rlppinv en-US