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
4.56 score 1 stars 3 dependents 326 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
3.95 score 1 stars 1 dependents 244 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
3.40 score 1 stars 333 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
3.40 score 1 stars 275 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 220 downloadsrpanelrev - Reverse the Order of Values of Time Series and Multidimensional Panels
Reverses the order of values of selected variables in time series and multidimensional panels, either by replacing the original values or by creating new variables with prefixed names. Optionally evaluates user-supplied pre-estimation, estimation, and post-estimation functions using the transformed data.
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data-managementpanel-datatime-series
2.70 score 1 stars 179 downloadsrpanelauto - Perform Automatic Estimation on Time Series in Multidimensional Panels
Provides a framework for automatic estimation of time-series models for univariate and multidimensional panel data. A user-supplied estimation function is applied independently to each time series, with optional processing before and after estimation. The function returns the transformed data together with the fitted models.
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automatic-modelspanel-data
2.70 score 1 stars 202 downloads