An implementation of Multi-Objective Decision Analysis algorithms

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

Repository for example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in R. These scripts provide useful examples for using JAGS with R2jags, the JAGS Wiener module, mixture modeling in JAGS, and Bayesian diagnostics in R.

The RMCDA package is a comprehensive toolkit for Multi-Criteria Decision Analysis (MCDA), designed to facilitate the evaluation and ranking of alternatives based on multiple criteria. The package provides methods for implementing various MCDA techniques, including pairwise comparisons, partial-order analysis, and dominance-based decision rules.

Scripts associated to the paper to be published

Model recovery analysis for intertemporal choice

This is an online repository gathering all codes employed for the analyses presented in the paper (Torres-Martos Á et al. (2024)). The purpose of this repository is to allow researchers to reproduce the Machine Learning pipeline presented in the paper, as well as to adapt provided codes for the analyses of their own datasets.

Analytical tool to help the company decide whether the employee will stay or not

Data Mining Project: Classification of the Car Evaluation Dataset.

This repository contains the code for the paper "Infection Rate Models for COVID-19: Model Risk and Public Health News Sentiment Exposure Adjustments", by Ioannis Chalkiadakis, Kevin Hongxuan Yan, Gareth W. Peters, Pavel V. Shevchenko.

Replication of simulations and results from Chapelle, O., & Li, L. (2011). An empirical evaluation of Thompson sampling. Advances in neural information processing systems, 2249-2257.

RMCriteria is a package to solve Multiple-Criteria Decision Analysis (MCDA) problems. For now, it only supports Promethee methods, but other methods may be developed in the future.

Yes/No as a service in R.

Analysis and modelling of effort-based choice in economic decision-making

Analyses scripts used for Algermissen, J. & den Ouden, H. E. (2024). Pupil dilation reflects effortful action invigoration in overcoming aversive Pavlovian biases. Cognitive, Affective, & Behavioral Neuroscience

For files & code related to the EDI study ("Effort, Decision-making, and Interoception").

Task timing and preference analysis

Application Web for Health Economics Decision Making

Analyses scripts used for Algermissen, J. & den Ouden, H. E. M. (2024). High stakes slow responding, but do not help overcome Pavlovian biases in humans. Learning & Memory.

Analyses scripts used for Algermissen, J. & den Ouden, H. E. (2023). Goal-directed recruitment of Pavlovian biases through selective visual attention. Journal of Experimental Psychology: General.