Add to ORKGThe lme4 package provides R functions to fit and analyze several different types of mixed-effects models, including linear mixed models, generalized linear mixed models and nonlinear mixed models. In this vignette we describe the formulation of these models and the compu-tational approach used to evaluate or approximate the log-likelihood of a model/data/parameter value combination.
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
The lme4 package provides R functions to fit and analyze several different types of mixed-effects mo...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mix...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in linear mi...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in linear mi...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in linear mi...
Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to margina...
The appendix explains how we computed linear mixed effect models for data structured in different le...
The appendix explains how we computed linear mixed effect models for data structured in different le...
Linear Mixed Model (LMM) is an extended regression method that is used for longitudinal data which h...
Ana Alves Francisco's Mixed effect models code for R. With an explanation of how to use it by Douwe ...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
The lme4 package provides R functions to fit and analyze several different types of mixed-effects mo...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mix...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in linear mi...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in linear mi...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the pa-rameters in linear mi...
Maximum likelihood estimation of generalized linear mixed models (GLMMs) is difficult due to margina...
The appendix explains how we computed linear mixed effect models for data structured in different le...
The appendix explains how we computed linear mixed effect models for data structured in different le...
Linear Mixed Model (LMM) is an extended regression method that is used for longitudinal data which h...
Ana Alves Francisco's Mixed effect models code for R. With an explanation of how to use it by Douwe ...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...