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glmmrBase - Generalised Linear Mixed Models in R

Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more.

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cpp

5.50 score 5 stars 3 dependents 5 scripts 628 downloads

glmmrOptim - Approximate Optimal Experimental Designs Using Generalised Linear Mixed Models

Optimal design analysis algorithms for any study design that can be represented or modelled as a generalised linear mixed model including cluster randomised trials, cohort studies, spatial and temporal epidemiological studies, and split-plot designs. See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a detailed manual on model specification. A detailed discussion of the methods in this package can be found in Watson, Hemming, and Girling (2023) <doi:10.1177/09622802231202379>.

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cppopenmp

3.70 score 1 stars 419 downloads

marginme - Estimation of Relative Risks, Risk Differences, and Marginal Effects from Mixed Models Using Marginal Standardization

The package provides a function to estimate relative risks, risk differences, and partial effects from mixed model. Marginalisation over random effect terms is accomplished using Markov Chain Monte Carlo.

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2.00 score 1 stars 185 downloads