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rlppinv: Linear Programming via Regularized Least Squares

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.

Version:	0.1.0
Depends:	R (≥ 4.2)
Imports:	rclsp
Suggests:	testthat (≥ 3.0.0)
Published:	2025-12-03
DOI:	10.32614/CRAN.package.rlppinv
Author:	Ilya Bolotov [aut, cre]
Maintainer:	Ilya Bolotov <ilya.bolotov at vse.cz>
BugReports:	https://github.com/econcz/rlppinv/issues
License:	MIT + fileLICENSE
URL:	https://github.com/econcz/rlppinv
NeedsCompilation:	no
Language:	en-US
Materials:	README
CRAN checks:	rlppinv results

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