active-inference-in-r

name Active Inference in R description R implementation of Active Inference with belief updating, free energy minimization, and policy selection Active Inference in R Overview This skill provides a complete Active Inference implementation in R, demonstrating Bayesian belief updating, variational free energy calculation, and expected free energy-based policy selection. Core Algorithms Belief Updating : Bayesian inference using observation likelihoods to update posterior beliefs Free Energy Calculation : KL divergence between posterior beliefs and prior distribution Policy Selection : Softmax action selection over expected free energy per action Perception-Action Loop : Iterative sense → infer → act cycle with generative model Key Files active_inference.R — Source implementation run.sh — Execution script (handles compilation if needed) README.md — Usage documentation and requirements Usage cd 0_CONTEXT/Computer_Languages/R/ ./run.sh Language-Specific Features Native matrix/array operations Scientific computing ecosystem Built-in visualization support Integration Tested via master_controller.py test r Benchmarked via benchmark_suite.py Listed in languages.json under category "Scientific" Prerequisites See README.md for R-specific installation requirements.