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

• Main Authors: Casey M. Jelsema, Shyamal D. Peddada
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2016-11-01
• Series: Journal of Statistical Software
• Subjects: distribution free; linear inequality constraints; linear fixed effects models; linear mixed effects models; order restricted inference; residual bootstrap; R
• Online Access: https://www.jstatsoft.org/index.php/jss/article/view/2922

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.