Name: Kyle M. Lang
Type: User
Company: Utrecht University Department of Methodology and Statistics
Bio: I am an assistant professor at Utrecht University. I teach statistics and data science courses, and I primarily study methods for missing data treatment.
Location: Tilburg, Noord Brabant, Nederlands
Blog: https://www.kylemlang.com
Kyle M. Lang's Projects
BachelorThesis2021
This repository holds the code to synthesize the NL burn data for Nancy van Loey's project.
Code review sessions at the Tilburg University Methodology and Statistics department
DisplayLink driver installer for Debian and Ubuntu based Linux distributions.
Extensible Virtual Display Interface
flexplot: graphical data analysis
Files for the manuscript Identifying Predictors of Missingness with Feature Selection Algorithms
Fundamental Techniques in Data Science with R
Visualize incomplete and imputed data with the R package `ggmice`
This repository holds the code used in the analysis of Fukuda et al. (2023).
This repository holds the code for my DV Imputation Simulation.
This repository will hold all of the materials for the Utrecht University Winter School course: Introduction to R.
JASP aims to be a complete statistical package for both Bayesian and Frequentist statistical methods, that is easy to use and familiar to users of SPSS
The basic R pkg for JASP
The Descriptives Module
The Factor Module
A module to help learn bayesian statistics
The PROCESS module
The Reliability Module
The Visual Modeling Module
Repository for hosting my personal website.
This repository will hold the materials for the Lavaan E-Learning course given as part of the Utrecht University Summer School course.
This repository contains various sets of lecture slides that I have developed over they years.
Repository hosting development of the MIBRR R package
This repository holds the analysis code for L.P. Hulsbosch's mindfulness intervention study.
This repository will hold all of the materials for the Utrecht University Winter School course: Missing Data in R.
This repository holds the code used to run the analyses reported in Moore, Lang, and Grandfield (2020). Maximizing data quality and shortening survey time: Three-form planned missing data survey design