worked R examples

GLMMs with adaptive Gaussian quadrature

Julia package for fitting mixed-effects models with flexible random/repeated covariance structure.

The book "Embrace Uncertainty: Fitting Mixed-Effects Models with Julia"

code repository for Lagattuta et al. "Repertoire analyses reveal T cell receptor sequence features that influence T cell fate." Nat. Imm. (2022)

General pipeline used for analyzing EEG data where Raw EEG data gets transformed into ERPS and Stats are done in R (Mixed effects models)

Steve's Miscellaneous Functions

GMPE-estimation implements a one-stage estimation algorithm to estimate ground-motion prediction equations (GMPE) with spatial correlation. It also quantifies the uncertainty of spatial correlation and intensity measure predictions.

Generalised Mixed Effects Model. Now any machine learning model can have random effects.

Fast cluster-based permutation test for densely-sampled, multi-level time series data

Simple Mixed Effect Models and Censoring

This repository commits to the application of biostatistics knowledge on clinical, randomized trials and observational studies.

Modeling correlated count data

Life Expectancy Prediction by Country with supervised learning, mixed effect effect modeling, and clustering.

Simple mixed-effects growth models applied to tumor growth (modeling the effect of treatment and measurement method)

R scripts for analysing data from a language production study with bilinguals

Early Growth Genetics Longitudinal Analysis

Website for Biostat 200C (Methods in Biostatistics C)

Is it possible to predict the popularity of a song based on its technical features? Which aspect of a song should an artist focus on to get more visibility? Could record labels or a new up-and-coming artist benefit from these data-driven and data-informed decisions?