https://econcz.r-universe.devRecent package updates in econczR-universehttps://github.com/econcz.pnghttps://econcz.r-universe.devSun, 07 Jun 2026 22:32:58 GMTilya.bolotov@vse.cz (Ilya Bolotov)The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net).https://github.com/r-universe/econcz/actions/runs/27107570234Sun, 07 Jun 2026 22:32:58 GMTrtmpinv2.0.0successhttps://econcz.r-universe.devhttps://github.com/econcz/rtmpinvilya.bolotov@vse.cz (Ilya Bolotov)Provides an interactive wrapper for the 'tmpinv()' function from the 'rtmpinv' package with options extending its functionality to pre- and post-estimation processing and streamlined incorporation of prior cell information. The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net).https://github.com/r-universe/econcz/actions/runs/27107422964Sun, 07 Jun 2026 22:30:51 GMTrtmpinvi2.0.0successhttps://econcz.r-universe.devhttps://github.com/econcz/rtmpinviilya.bolotov@vse.cz (Ilya Bolotov)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.https://github.com/r-universe/econcz/actions/runs/27107569714Sun, 07 Jun 2026 22:13:37 GMTrlppinv2.0.0successhttps://econcz.r-universe.devhttps://github.com/econcz/rlppinvilya.bolotov@vse.cz (Ilya Bolotov)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.https://github.com/r-universe/econcz/actions/runs/27107420149Sun, 07 Jun 2026 21:35:59 GMTrclsp2.0.0successhttps://econcz.r-universe.devhttps://github.com/econcz/rclspilya.bolotov@vse.cz (Ilya Bolotov)Convert text into target classifications (e.g., ISO 3166-1) using a JSON mapping with regular expressions. Provides helpers to return the full mapping and associated metadata.https://github.com/r-universe/econcz/actions/runs/27102770560Sun, 07 Jun 2026 19:21:23 GMTrconvertu1.1.0successhttps://econcz.r-universe.devhttps://github.com/econcz/rconvertu