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Linear and Nonlinear Mixed Effects Models

Fit and compare Gaussian linear and nonlinear mixed-effects models.

Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

MCMC Generalised Linear Mixed Models

Fits Multi-response Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques.

Tidying Methods for Mixed Models

Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.

Generalized Additive Mixed Models using 'mgcv' and 'lme4'

Estimate generalized additive mixed models via a version of function gamm() from 'mgcv', using 'lme4' for estimation.

Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models

Test in mixed effects models. Attention is on mixed
effects models as implemented in the 'lme4' package. For linear
mixed models, this package implements (1) a parametric bootstrap
test, (2) a Kenward-Roger-typ modification of F-tests for linear
mixed effects models and (3) a Satterthwaite-type modification of
F-tests for linear mixed effects models. The package also
implements a parametric bootstrap test for generalized linear
mixed models. The facilities of the package are documented in the
paper by Halehoh and Højsgaard, (2012,

Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Generalized Cross Validation and similar, or using iterated
nested Laplace approximation for fully Bayesian inference. See
Wood (2017)

Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

Fits generalized linear mixed models for a single grouping factor under
maximum likelihood approximating the integrals over the random effects with an
adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995)

Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects and unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016