Josué M's repositories
Analytics_HR_Attrition
Exploratory Data Analysis, and predictions using machine learning techniques
awesome-ggplot2
A curated list of awesome ggplot2 tutorials, packages etc.
bayes-concept
Research report providing the technical details of a Bayesian framework for diagnostic assessments
Bayes-Factor-Design-Analysis-App
A Shiny App for Bayes Factor Design Analysis
BayesCog_Wien
teaching materials univie
bayesian_changepoint_detection
Methods to get the probability of a changepoint in a time series.
cmdstan_map_rect_tutorial
Beginner tutorial for using cmdstan with multithreading
cognitive_models
Collection of my implementations of computational models of cognition
cursoABMPythonPublic
Curso de intro a la programación y modelos basados en agentes - CIDE/LNPP - Otoño 2019
designing.ggplots
Install Workshop Materials for Designing ggplots
designing_ggplots_slides
Slides for the workshop Designing ggplots
ggplot2_workshop
Material for "Drawing Anything with ggplot2" workshop
knitr-examples
A collection of knitr examples
paper-symlmm
Manuscript for Symbolic Formulae for Linear Mixed Models
post--visual-exploration-gaussian-processes
A Visual Exploration of Gaussian Processes
PsychoPy-Sandwich-Demo-Guide
This is an online PsychoPy experiment I built recently using the PsychoPy builder, and hosted online with pavlovia.org. I've attempted to document all the steps needed in building the experiment, and getting it to work online, but if there are other unanswered questions I recommend jumping over to the PsychoPy discourse: https://discourse.psychopy.org/
r-irt
A high-level introduction to using R for Item Response Theory
Radboud-Summerschool-Complexity-Methods-2019
Radboud Summerschool 2019: Complexity Methods for Behavioural Science
rdatatable-cookbook
Recipes for using R's data.table package
SEM-code-examples
Code examples for Structural Equation Models using various software packages
SPM-IRT-models
This repository contains all materials for the paper "Analysing Standard Progressive Matrics (SPM-LS) with Bayesian Item Response Models" by Paul Bürkner and Marie Beisemann.
TenSimpleRulesModeling
Code for the figures in the "Ten Simple Rules for Computational Modeling of Behavioral Data" paper.