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Least-Squares Means: The<i>R</i>Package<b>lsmeans</b>

Russell V. Lenth · Journal of Statistical Software · 2016

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Author / responsible party
Russell V. Lenth
Publisher
Journal of Statistical Software
Year
2016
Language
English

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Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The lsmeans package (Lenth 2016) provides a simple way of obtaining least-squares means and contrasts thereof. It supports many models fitted by R (R Core Team 2015) core packages (as well as a few key contributed ones) that fit linear or mixed models, and provides a simple way of extending it to cover more model classes.

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APA 7

Lenth, R. V. (2016). Least-Squares Means: TheRPackagelsmeans. https://doi.org/10.18637/jss.v069.i01

MLA

Lenth, Russell V. "Least-Squares Means: TheRPackagelsmeans." 2016. https://doi.org/10.18637/jss.v069.i01.

Chicago

Lenth, Russell V. 2016. "Least-Squares Means: TheRPackagelsmeans.". https://doi.org/10.18637/jss.v069.i01.

Harvard

Lenth, R. V. 2016, Least-Squares Means: TheRPackagelsmeans, Journal of Statistical Software, available at: https://doi.org/10.18637/jss.v069.i01 [Accessed 8 Aug. 2026].

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Title
Least-Squares Means: The<i>R</i>Package<b>lsmeans</b>
Author / contributors
Russell V. Lenth
Publisher
Journal of Statistical Software
Publication year
2016
Language
English

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Least-squares function approximation; Simple (philosophy); R package; Computer science; Core (optical fiber); Cover (algebra); Linear model; Applied mathematics; Mathematics; Algorithm; Statistics; Computational science
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