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Tables and Graphs for Mixed Models for Repeated Measures (MMRM)
Mixed models for repeated measures (MMRM) are a popular
choice for analyzing longitudinal continuous outcomes in randomized
clinical trials and beyond; see for example Cnaan, Laird and Slasor
(1997)
Bayesian Robust Generalized Mixed Models for Longitudinal Data
To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or < https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.
Fit a Cosinor Model Using a Generalized Mixed Modeling Framework
Allows users to fit a cosinor model using the 'glmmTMB' framework.
This extends on existing cosinor modeling packages, including 'cosinor'
and 'circacompare', by including a wide range of available link functions
and the capability to fit mixed models. The cosinor model is described by
Cornelissen (2014)
Exact (Restricted) Likelihood Ratio Tests for Mixed and Additive Models
Rapid, simulation-based exact (restricted) likelihood ratio tests for testing the presence of variance components/nonparametric terms for models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(), mgcv::gamm() and SemiPar::spm().
Generalized Additive Mixed Model Analysis via Slice Sampling
Uses a slice sampling-based Markov chain Monte Carlo to
conduct Bayesian fitting and inference for generalized additive
mixed models. Generalized linear mixed models and generalized
additive models are also handled as special cases of generalized
additive mixed models. The methodology and software is described
in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand
Journal of Statistics, 60, 279-330
Generalized Linear Mixed Model Analysis via Expectation Propagation
Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018)
Isoscape Computation and Inference of Spatial Origins using Mixed Models
Building isoscapes using mixed models and inferring the geographic origin of samples based on their isotopic ratios. This package is essentially a simplified interface to several other packages which implements a new statistical framework based on mixed models. It uses 'spaMM' for fitting and predicting isoscapes, and assigning an organism's origin depending on its isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for plotting the maps produced with 'terra' using 'lattice'.
General Linear Mixed Models for Gene-Level Differential Expression
Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine
A Fast Laplace Method for Spatial Generalized Linear Mixed Model
Fitting a fast Laplace approximation for Spatial Generalized Linear Mixed Model as described in Park and Lee (2021) < https://github.com/sangwan93/fastLaplace/blob/main/FastLaplaceMain.pdf>.
Fast Functional Mixed Models using Fast Univariate Inference
Implementation of the fast univariate inference approach (Cui et al. (2022)