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.
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convex-optimizationestimatorsgeneralized-inverseleast-squaresregularization
4.50 score 1 stars 3 dependents 577 downloadsrtmpinv - Tabular Matrix Problems via Pseudoinverse Estimation
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).
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convex-optimizationgeneralized-inverseleast-squaresregularizationtabular-matrix-problems
3.88 score 1 stars 1 dependents 364 downloadsrtmpinvi - Interactive Tabular Matrix Problems via Pseudoinverse Estimation
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).
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least-squareslinear-programmingpseudoinversesingular-value-decomposition
3.30 score 1 stars 525 downloadsrlppinv - 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.
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convex-optimizationgeneralized-inverseleast-squareslinear-programingregularization
3.30 score 1 stars 526 downloadsrconvertu - From/to Classification Converter
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.
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country-codesdata-managementiso
3.00 score 1 stars 325 downloads