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Post-Estimation Functions for Generalized Linear Mixed Models
Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).
Fast Fitting of Stable Isotope Mixing Models with Covariates
Fast fitting of Stable Isotope Mixing Models in R. Allows for the inclusion of covariates. Also has built-in summary functions and plot functions which allow for the creation of isospace plots. Variational Bayes is used to fit these models, methods as described in: Tran et al., (2021)
Maximum Likelihood Estimation for Generalized Linear Mixed Models
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005)
Mixed Model Association Test for GEne-Environment Interaction
Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020)
Scale Mixture of Skew-Normal Linear Mixed Models
It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021)
Estimate (Generalized) Linear Mixed Models with Factor Structures
Utilizes the 'lme4' and 'optimx' packages (previously the optim()
function from 'stats') to estimate (generalized) linear mixed models (GLMM)
with factor structures using a profile likelihood approach, as outlined in
Jeon and Rabe-Hesketh (2012)
Robust Bayesian Longitudinal Regularized Semiparametric Mixed Models
Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'.
Generalized Fiducial Inference for Normal Linear Mixed Models
Simulation of the generalized fiducial distribution for
normal linear mixed models with interval data. Fiducial inference is
somehow similar to Bayesian inference, in the sense that it is based
on a distribution that represents the uncertainty about the
parameters, like the posterior distribution in Bayesian statistics. It
does not require a prior distribution, and it yields results close to
frequentist results. Reference: Cisewski and Hannig (2012)
Variable Selection in Linear Mixed Models for SNP Data
Fit penalized multivariable linear mixed models with a single
random effect to control for population structure in genetic association
studies. The goal is to simultaneously fit many genetic variants at the
same time, in order to select markers that are independently associated
with the response. Can also handle prior annotation information,
for example, rare variants, in the form of variable weights. For more
information, see the website below and the accompanying paper:
Bhatnagar et al., "Simultaneous SNP selection and adjustment for
population structure in high dimensional prediction models", 2020,
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)