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

Covers the basics of mixed models, mostly using @lme4

A document introducing generalized additive models.📈

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

Mixed models @lme4 + custom covariances + parameter constraints

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

Workshop on using Mixed Models with R

👓 Functions related to R visualizations

Functions for using mgcv for mixed models. 📈

The main aim of this code is to measure the co-movements along 9 different currencies.

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

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

Demonstration of alternatives to lme4

An R package for I-prior 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.

Monte Carlo Simulation comparing the performance of various estimators for panel data with binary dependent variable models

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

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