Functions for using mgcv for mixed models. 📈
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Updated
Oct 20, 2020 - R
Functions for using mgcv for mixed models. 📈
This package is used to analyse datasets of different HPO-algorithms performing on multiple benchmarks.
Analysis of data from the psycholinguistics experiment (visual lexical decision task) with three different ML techniques: LMER (Linear Mixed-Effect Regression), GLMER (Generalized Linear Mixed-Effect Regression) & GAMMs (Generalized Additive Mixed Modeling).
R package for computing, extracting, and visualizing response-contingencies
During the winter semester 2019/2020 (November, 2019) I lectured at the course "Advanced Statistics in R" at the Goethe University Frankfurt, Germany. On this page you can find codes used for this course.
Analysis of driver cutting behavior using lmer.
course sub-material for Multi-level Modeling
Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model
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.
AirBnB, Austin City Limits, Hierarchical Models, Random Effects
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