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broom.mixed — by Ben Bolker, 8 months ago

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.

MCMCglmm — by Jarrod Hadfield, 5 months ago

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.).

gamm4 — by Simon Wood, 3 years ago

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.

pbkrtest — by Søren Højsgaard, 2 years ago

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, ). Please see 'citation("pbkrtest")' for citation details.

lme4 — by Ben Bolker, a month ago

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".

GLMMadaptive — by Dimitris Rizopoulos, 10 months ago

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) .

lmerTest — by Rune Haubo Bojesen Christensen, 2 years ago

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.

lqmm — by Marco Geraci, 8 months ago

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) . A vignette is given in Geraci (2014, Journal of Statistical Software) and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses.

lcmm — by Cecile Proust-Lima, 5 months ago

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) ).

gaston — by Hervé Perdry, 2 years ago

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 .