🌍 Implement climate prediction models to transform global data into localized forecasts for effective water resource management and impact assessment.

Python-first access to R’s brms with proper parameter names, ArviZ support, and cmdstanr performance. The easiest way to run brms models from Python.

A powerful library extending VBA with over 100 functions for math, stats, finance, and data manipulation. It supports matrix operations, and user-defined functions, enhancing automation and analysis within Microsoft Office and LibreOffice environments for data management, financial calculations, an more.

Generalized linear models in Julia

UChicago STAT Courses: 220, Methods and Applications; 224, Applied Regression Analysis

Sub-package of spatstat containing code for linear networks

simstudy: Illuminating research methods through data generation

Workshop on pipeline development and model deployment onto Kubernetes via Docker using R.

AI-powered max-tree based source detection and parameter extraction software for astronomical image data processing.

coevolve R package for Bayesian generalized dynamic phylogenetic models using Stan

🖊️ Xiangyun's personal website

Basic statistics for Julia

A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.

Study of scientific problems using computational methods; it combines computer science, physics and applied mathematics to develop scientific solutions to complex problems.

Umbrella package of the 'spatstat' family................

An R package providing a GUI ('shiny' app) for the R package 'brms'.

MS Data Science capstone repository for "Predicting Stock Market Index Direction Using ARIMA-Augmented Quantum Random Forest Models"

📊 Computation and processing of models' parameters

A Julia package for fitting (statistical) mixed-effects models