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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.
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.).
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,
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".
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)
Tests in Linear Mixed Effects Models
Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.
Linear Quantile Mixed Models
Functions to fit quantile regression models for hierarchical
data (2-level nested designs) as described in Geraci and
Bottai (2014, Statistics and Computing)
Extended Mixed Models Using Latent Classes and Latent Processes
Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017)
Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models
Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al