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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 Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models
Computes p-values based on (a) Satterthwaite or
Kenward-Rogers degree of freedom methods and (b) parametric bootstrap
for mixed effects models as implemented in the 'lme4'
package. Implements parametric bootstrap test for generalized linear
mixed models as implemented in 'lme4' and generalized linear
models. The package is documented in the paper by Halekoh and
Højsgaard, (2012,
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.
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)
Linear Mixed Models
It implements Expectation/Conditional Maximization Either (ECME) and rapidly converging algorithms as well as Bayesian inference for linear mixed models, which is described in Schafer, J.L. (1998) "Some improved procedures for linear mixed models". Dept. of Statistics, The Pennsylvania State University.
Mixed Effects Cox Models
Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002.
Parallel Linear Mixed Model
Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.
Some Algorithms for Mixed Models
This program can be used to fit Gaussian linear mixed models (LMM). Univariate and multivariate response models, multiple variance components, as well as, certain correlation and covariance structures are supported. In many occasions, the user can pick one of the several mixed model fitting algorithms, which are explained further in the details section. Some algorithms are specific to certain types of models (univariate or multivariate, diagonal or non-diagonal residual, one or multiple variance components, etc,...).
Linear Mixed Model Solver
An efficient and flexible system to solve sparse mixed model
equations. Important applications are the use of splines to model spatial or temporal
trends as described in Boer (2023). (