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coneproj: An R Package for the Primal or Dual Cone Projections with Routines for Constrained Regression

Author

Listed:
  • Liao, Xiyue
  • Meyer, Mary C.

Abstract

The coneproj package contains routines for cone projection and quadratic programming, plus applications in estimation and inference for constrained parametric regression and shape-restricted regression problems. A short routine check_irred is included to check the irreducibility of a matrix, whose rows are supposed to be a set of cone edges used by coneA or coneB. For the coneA and coneB functions, the vector to project is provided by the user, along with the cone specification and a weight vector. For coneA, a constraint matrix is specified to define the cone, and for coneB, the cone edges are provided. The coneA and coneB algorithms have been coded and compiled in C++, and are called by R. The qprog function transforms a quadratic programming problem into a cone projection problem and calls coneA. The constreg function does estimation and inference for parametric least-squares regression with constraints on the parameters (using coneA). A p value for the “one-sided” test is provided. The shapereg function uses coneB to provide a least-squares estimator for a regression function with several choices of constraints including isotonic and convex regression functions, as well as estimates of parametrically modeled covariate effects. Results from hypothesis tests for significance of the effects are also provided. This package is now available from the Comprehensive R Archive Network at http://CRAN.R-project.org/package=coneproj.

Suggested Citation

  • Liao, Xiyue & Meyer, Mary C., 2014. "coneproj: An R Package for the Primal or Dual Cone Projections with Routines for Constrained Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i12).
    • Handle: RePEc:jss:jstsof:v:061:i12
    DOI: http://hdl.handle.net/10.18637/jss.v061.i12
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    References listed on IDEAS

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    1. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    2. de Leeuw, Jan & Hornik, Kurt & Mair, Patrick, 2009. "Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i05).
    3. Mary C. Meyer, 2003. "A test for linear versus convex regression function using shape-restricted regression," Biometrika, Biometrika Trust, vol. 90(1), pages 223-232, March.
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    Cited by:

    1. Sanjida Tasnim, 2021. "Use of Shape Restricted Regression Methods for Fitting Model of Per Capita GDP: A Global Economic Scenario of 2018," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(4), pages 1-52, July.
    2. Liu, Ruixuan & Yu, Zhengfei, 2022. "Sample selection models with monotone control functions," Journal of Econometrics, Elsevier, vol. 226(2), pages 321-342.
    3. Han, Kyunghee & Park, Yeonjoo & Kim, Soo-Young, 2025. "Statistical inference for partially shape-constrained function-on-scalar linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 211(C).

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