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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>.
Fitting Linear Quantile Regression Mixed Models with Relationship Matrix
Fit a quantile regression mixed model involved Relationship Matrix using a sparse implementation of the Frisch-Newton interior-point algorithm as described in Portnoy and Koenker (1977, Statistical Science) < https://www.jstor.org/stable/2246216>.
Fast Functional Mixed Models using Fast Univariate Inference
Implementation of the fast univariate inference approach (Cui et al. (2022)
High Dimensional Penalized Generalized Linear Mixed Models (pGLMM)
Fits high dimensional penalized generalized linear
mixed models using
the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm.
The purpose of the package is to perform variable selection on both the fixed and
random effects simultaneously for generalized linear mixed models.
The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and
supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm
is described in Rashid et al. (2020)
Build Network Based on Linear Mixed Models from EHRs
Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015)
Spaghetti-Plot Fixed and Random Effects of Linear Mixed Models
Plot both fixed and random effects of linear mixed models, multilevel models in a single spaghetti plot. The package allows to visualize the effect of a predictor on a criterion between different levels of a grouping variable. Additionally, confidence intervals can be displayed for fixed effects. Calculation of predicted values of random effects allows only models with one random intercept and/or one random slope to be plotted. Confidence intervals and predicted values of fixed effects are computed using the 'ggpredict' function from the 'ggeffects' package. Lüdecke, D. (2018)
Multivariate Generalized Linear Mixed Models for Ranking Sports Teams
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018)
Flexible Tree Taper Curves Based on Semiparametric Mixed Models
Implementation of functions for fitting taper curves (a semiparametric
linear mixed effects taper model) to diameter measurements along stems. Further
functions are provided to estimate the uncertainty around the predicted curves,
to calculate timber volume (also by sections) and marginal (e.g., upper) diameters.
For cases where tree heights are not measured, methods for estimating
additional variance in volume predictions resulting from uncertainties in
tree height models (tariffs) are provided. The example data include the taper
curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees
and a subset of section-wise diameter measurements of these trees. The functions
implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013)