Functions for using mgcv for mixed models. 📈

Applied Regression In R

DNA methylation (DNAm) analysis using Illumina Methylation arrays (EPICv2, EPIC and 450K). It includes preprocessing, quality control, phenotype merging, and statistical modeling (GLM and LMM) for any phenotype using both minfi, ENmix and wateRmelon frameworks.

This code fits a series of logit mixed models to data from Boyd and Goldberg (2011), Experiment 1. All models specify the maximal random effects structure, as advocated by Barr et al. (2013). All results from Boyd and Goldberg are replicated.

Analysis of driver cutting behavior using lmer.

running lmer in R for ferret behavioural data

R package for computing, extracting, and visualizing response-contingencies

course sub-material for Multi-level Modeling

Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model

This package is used to analyse datasets of different HPO-algorithms performing on multiple benchmarks.

AirBnB, Austin City Limits, Hierarchical Models, Random Effects

Mixed models analysis collaborative project: experimental design, data collection, and mixed model analysis.

Repository for storing and analyzing golf scores using linear mixed effects modeling and machine learning