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CLME: An R Package for Linear Mixed Effects Models under Inequality Constraints

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  • Jelsema, Casey M.
  • Peddada, Shyamal D.

Abstract

In many applications researchers are typically interested in testing for inequality constraints in the context of linear fixed effects and mixed effects models. Although there exists a large body of literature for performing statistical inference under inequality constraints, user friendly statistical software implementing such methods is lacking, especially in the context of linear fixed and mixed effects models. In this article we introduce CLME, a package in the R language that can be used for testing a broad collection of inequality constraints. It uses residual bootstrap based methodology which is reasonably robust to non-normality as well as heteroscedasticity. The package is illustrated using two data sets. The package also contains a graphical user interface built using the shiny package.

Suggested Citation

  • Jelsema, Casey M. & Peddada, Shyamal D., 2016. "CLME: An R Package for Linear Mixed Effects Models under Inequality Constraints," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i01).
    • Handle: RePEc:jss:jstsof:v:075:i01
    DOI: http://hdl.handle.net/10.18637/jss.v075.i01
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    References listed on IDEAS

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    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    2. Laura Farnan & Anastasia Ivanova & Shyamal D Peddada, 2014. "Linear Mixed Effects Models under Inequality Constraints with Applications," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-8, January.
    3. 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).
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