lme4
R package lme4: Linear mixed-effects models using S4 classes , Fit linear and generalized linear mixed-effects models. This is the implementation of lme4 available on CRAN and developed up to 2011.
(Source: http://cran.r-project.org/web/packages)
Keywords for this software
References in zbMATH (referenced in 181 articles , 1 standard article )
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- Thomas, Abin; Vishwakarma, Gajendra K.; Bhattacharjee, Atanu: Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers (2021)
- Aaron Cochrane: TEfits: Nonlinear regression for time-evolving indices (2020) not zbMATH
- Cho, Sun-Joo; Brown-Schmidt, Sarah; De Boeck, Paul; Shen, Jianhong: Modeling intensive polytomous time-series eye-tracking data: a dynamic tree-based item response model (2020)
- Cunen, Céline; Walløe, Lars; Hjort, Nils Lid: Focused model selection for linear mixed models with an application to whale ecology (2020)
- Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
- Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
- Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)
- Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
- Yoon, Hwan-Jin; Welsh, Alan H.: On the effect of ignoring correlation in the covariates when fitting linear mixed models (2020)
- Ann-Kristin Kreutzmann; Sören Pannier; Natalia Rojas-Perilla; Timo Schmid; Matthias Templ; Nikos Tzavidis: The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators (2019) not zbMATH
- Baey, Charlotte; Cournède, Paul-Henry; Kuhn, Estelle: Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models (2019)
- Bon, Joshua J.; Murray, Kevin; Turlach, Berwin A.: Fitting monotone polynomials in mixed effects models (2019)
- Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
- Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
- Heathcote, Andrew; Holloway, Eleanor; Sauer, James: Confidence and varieties of bias (2019)
- Heck, Daniel W.: Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: a comment on Nathoo, Kilshaw, and Masson (2018) (2019)
- Hui, F. K. C.; Müller, Samuel; Welsh, A. H.: Testing random effects in linear mixed models: another look at the F-test (with discussion) (2019)
- Ippel, L.; Kaptein, M. C.; Vermunt, J. K.: Online estimation of individual-level effects using streaming shrinkage factors (2019)
- Lee, Wonyul; Miranda, Michelle F.; Rausch, Philip; Baladandayuthapani, Veerabhadran; Fazio, Massimo; Downs, J. Crawford; Morris, Jeffrey S.: Bayesian semiparametric functional mixed models for serially correlated functional data, with application to glaucoma data (2019)
- Marino, Maria Francesca; Ranalli, Maria Giovanna; Salvati, Nicola; Alfò, Marco: Semiparametric empirical best prediction for small area estimation of unemployment indicators (2019)