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Bayesian Variable Selection and Model Choice for Generalized Additive Mixed Models
Bayesian variable selection, model choice, and regularized estimation for (spatial) generalized additive mixed regression models via stochastic search variable selection with spike-and-slab priors.
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,...).
Bayesian Mixing Models in R
Creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR' is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. 'MixSIAR' incorporates several years of advances since 'MixSIR' and 'SIAR'.
Kernel Ridge Mixed Model
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145.
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.
A Stable Isotope Mixing Model
Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010
Stable Isotope Mixing Model
Estimates diet contributions from isotopic sources using JAGS. Includes estimation of concentration dependence and measurement error.
Latent Factor Mixed Models
Fast and accurate inference of
gene-environment associations (GEA) in genome-wide studies
(Caye et al., 2019,
GEMMA Multivariate Linear Mixed Model
Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (< https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package 'gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.