Additional linear models including instrumental variable and panel data models that are missing from statsmodels.

ggplot-based graphics and useful functions for GAMs fitted using the mgcv package

Covers the basics of mixed models, mostly using @lme4

Testing differences in cell type proportions from single-cell data.

A document introducing generalized additive models.📈

An R package for extracting results from mixed models that are easy to use and viable for presentation.

👓 Functions related to R visualizations

Mixed models @lme4 + custom covariances + parameter constraints

Workshop on using Mixed Models with R

Demonstration of alternatives to lme4

Functions for using mgcv for mixed models. 📈

Illustrate CR models with individual heterogeneity (multistate, random-effect, finite-mixture)

Using Fixed Effect, Random Effect and Hausman Taylor IV to estimate the impacts on wage

Copula Based Bivariate Beta-Binomial Model for Diagnostic Test Accuracy Studies

Connecting the Sustainable Development Goals with climate change and the energy transition

An R package for I-prior regression

Stata and R programs to automatically quasi-demean regressors following FGLS-RE or MLE-RE regression

a meta-analysis on the effect of intravenous magnesium on myocardial infarction

Raw files for a document providing an overview of mixed models from varying perspectives.

Cluster-specific logistic regression models for whether an NBA team will make the playoffs given the current statistics of that team. Specifically uses population averaged models (PA) based on generalized estimating equations (GEE); Also, uses cluster-specific (each team) random effects models