Power Analysis for Generalised Linear Mixed Models by Simulation

Calculate power for generalised linear mixed models, using simulation. Designed to work with models fit using the 'lme4' package. Described in Green and MacLeod, 2016 .

Data sets from "SAS System for Mixed Models"

Data sets and sample lmer analyses corresponding to the examples in Littell, Milliken, Stroup and Wolfinger (1996), "SAS System for Mixed Models", SAS Institute.

Inference of Linear Mixed Models Through MM Algorithm

The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. (2022) ).

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.

Fitting the Centered Autologistic and Sparse Spatial Generalized Linear Mixed Models for Areal Data

Provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) , the centered autologistic model of Caragea and Kaiser (2009) , and the Bayesian spatial filtering model of Hughes (2017) .

Spatial Generalised Linear Mixed Models for Areal Unit Data

Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, ) and Leroux model (Leroux et al., 2000, ). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.

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'.