lmerTest: Tests in Linear Mixed Effects Models. Different kinds of tests for linear mixed effects models as implemented in ’lme4’ package are provided. The tests comprise types I - III F tests for fixed effects, LR tests for random effects. The package also provides the calculation of population means for fixed factors with confidence intervals and corresponding plots. Finally the backward elimination of non-significant effects is implemented.

References in zbMATH (referenced in 9 articles , 1 standard article )

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  1. Ferri-García, Ramón; Castro-Martín, Luis; del Mar Rueda, María: Evaluating machine learning methods for estimation in online surveys with superpopulation modeling (2021)
  2. Nie, Yunlong; Opoku, Eugene; Yasmin, Laila; Song, Yin; Wang, Jie; Wu, Sidi; Scarapicchia, Vanessa; Gawryluk, Jodie; Wang, Liangliang; Cao, Jiguo; Nathoo, Farouk S.: Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics (2020)
  3. Qian, Tianchen; Klasnja, Predrag; Murphy, Susan A.: Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study (2020)
  4. Rasch, Dieter; Verdooren, Rob; Pilz, Jürgen: Applied statistics. Theory and problem solutions with R (2019)
  5. Sofie Pødenphant, Kasper Kristensen, Per B. Brockhoff: The Multiplicative Mixed Model with the mumm R package as a General and Easy Random Interaction Model Tool (2018) arXiv
  6. Alexandra Kuznetsova; Per Brockhoff; Rune Christensen: lmerTest Package: Tests in Linear Mixed Effects Models (2017) not zbMATH
  7. Chen, Ding-Geng (Din); Peace, Karl E.; Zhang, Pinggao: Clinical trial data analysis using R and SAS (2017)
  8. Russell Lenth: Least-Squares Means: The R Package lsmeans (2016) not zbMATH
  9. Huber, S.; Sury, D.; Moeller, K.; Rubinsten, O.; Nuerk, H.-C.: A general number-to-space mapping deficit in developmental dyscalculia (2015) MathEduc