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 17 articles , 1 standard article )

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  1. Kruse, René-Marcel; Silbersdorff, Alexander; Säfken, Benjamin: Model averaging for linear mixed models via augmented Lagrangian (2022)
  2. Rügamer, David; Baumann, Philipp F. M.; Greven, Sonja: Selective inference for additive and linear mixed models (2022)
  3. Benjamin Säfken, David Rügamer, Thomas Kneib, Sonja Greven: Conditional Model Selection in Mixed-Effects Models with cAIC4 (2021) not zbMATH
  4. Eshin Jolly: Pymer4: Connecting R and Python for Linear Mixed Modeling (2021) not zbMATH
  5. Ferri-García, Ramón; Castro-Martín, Luis; del Mar Rueda, María: Evaluating machine learning methods for estimation in online surveys with superpopulation modeling (2021)
  6. Kelty-Stephen, Damian G.; Furmanek, Mariusz P.; Mangalam, Madhur: Multifractality distinguishes reactive from proactive cascades in postural control (2021)
  7. Maullin-Sapey, Thomas; Nichols, Thomas E.: Fisher scoring for crossed factor linear mixed models (2021)
  8. Thomas P. Urbach; Andrey S. Portnoy: fitgrid: A Python package for multi-channel event-related time series regression modeling (2021) not zbMATH
  9. Tyler Morgan-Wall, George Khoury: Optimal Design Generation and Power Evaluation in R: The skpr Package (2021) not zbMATH
  10. 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)
  11. 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)
  12. Rasch, Dieter; Verdooren, Rob; Pilz, Jürgen: Applied statistics. Theory and problem solutions with R (2019)
  13. 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
  14. Alexandra Kuznetsova; Per Brockhoff; Rune Christensen: lmerTest Package: Tests in Linear Mixed Effects Models (2017) not zbMATH
  15. Chen, Ding-Geng (Din); Peace, Karl E.; Zhang, Pinggao: Clinical trial data analysis using R and SAS (2017)
  16. Russell Lenth: Least-Squares Means: The R Package lsmeans (2016) not zbMATH
  17. Huber, S.; Sury, D.; Moeller, K.; Rubinsten, O.; Nuerk, H.-C.: A general number-to-space mapping deficit in developmental dyscalculia (2015) MathEduc