Install HDDM 0.9 package(2022.07)

Computational Psychiatry

Install Hierachical Drift Diffusion Model via Conda.

HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.(https://github.com/hddm-devs/hddm)

How to install HDDM

  1. Create conda environment and activate it
conda create --name hdmm-0.9 python=3.9
conda activate hdmm-0.9

PS. If you want to set the environment as default, go to ~/.zshrc or ~/.bashrc and add this line: conda activate hddm-0.9.

PSS. To remove any environment, go back to base environment conda activate base, then remove what you want by conda env remove -n hdmm-0.9.

  1. Install package using conda(do not use pip, incompatibile issues!)
conda install cython
conda install pymc==2.3.8
conda install git pip
pip install git+https://github.com/hddm-devs/kabuki
pip install git+https://github.com/hddm-devs/hddm
# Optional
conda install torch torchvision torchaudio -->
  1. Test if HDDM successfully installed

Test the codes below in a python script.

import hddm

# Load data from csv file into a NumPy structured array
data = hddm.load_csv('simple_difficulty.csv')

# Create a HDDM model multi object
model = hddm.HDDM(data, depends_on={'v':'difficulty'})

# Create model and start MCMC sampling
model.sample(2000, burn=20)

# Print fitted parameters and other model statistics
model.print_stats()

# Plot posterior distributions and theoretical RT distributions
model.plot_posteriors()
model.plot_posterior_predictive()