{"library":"preliz","title":"Preliz Library","description":"Preliz is a Python library for exploring and eliciting probability distributions. It provides a flexible and object-oriented framework for defining, manipulating, and visualizing various distributions, commonly used for setting priors in Bayesian inference workflows. The current version is 0.24.0, and it maintains an active development and release cadence.","language":"python","status":"active","last_verified":"Fri Apr 17","install":{"commands":["pip install preliz"],"cli":null},"imports":["from preliz import Normal","import preliz"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import preliz\nimport numpy as np\n\n# Define a Normal distribution\nnorm_dist = preliz.Normal(mu=0, sigma=1)\n\n# Get PDF at a specific point\nprint(f\"PDF at x=0: {norm_dist.pdf(0):.3f}\")\n\n# Sample from the distribution\nsamples = norm_dist.rvs(size=100)\nprint(f\"Mean of 100 samples: {np.mean(samples):.2f}\")\nprint(f\"Std dev of 100 samples: {np.std(samples):.2f}\")\n\n# Access a scipy.stats compatible object (if needed)\nscipy_norm = norm_dist.to_scipy()\nprint(f\"Scipy PDF at x=0: {scipy_norm.pdf(0):.3f}\")","lang":"python","description":"Demonstrates how to define a distribution, calculate its PDF, sample from it, and convert it to a scipy.stats compatible object.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}