Code for modelling estimated deaths and cases for COVID19.
An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
A gallery of animations in statistics and utilities to create animations
🐳 Containerize R Markdown documents for continuous reproducibility
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
[NeurIPS 2023] Efficient Solver for ERM with PLQ Loss and Linear Constraints
Agent-native Stata bridge for empirical research — run DiD/IV/RDD and publication-ready tables from Claude Code, Jupyter, or VS Code on one token-economy result schema, with StatsPAI cross-validation. | 面向实证研究的智能体 Stata 桥接器——在 Claude Code、Jupyter、VS Code 中用一套省 token 的结果格式跑 DiD/IV/RDD 与出版级表格，并支持 StatsPAI 跨栈交叉验证。
Repo for code and small datasets related to Global Policy Lab's COVID-19 policy analysis. Read and share the acompanying article here:
Statistical computing library that aims to provide R-like experience in modern C++
A comprehensive collection of R scripts focusing on Probability and Statistics. Includes foundational data manipulation, Exploratory Data Analysis (EDA), statistical visualization (histograms, boxplots), and implementation of various probability distributions like Binomial, Normal, and Poisson. Ideal for showcasing core statistical computing skills
Intro to Statistical Data Analysis using R
📓 💪 An introductory workshop lecture on ensemble machine learning with pipelines using the sl3 R package
About Course webpage for UCLA Biostat 257 (Computational Methods for Biostatistical Research)
Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.
Source code for "STAT 3150 - Fall 2020" webpage
Statistical Computing Concepts for Public Health Researchers
Mathematical Foundations and Statistical Computing in R
Stata 18 Complete Statistical Setup — econometrics, panel data, survival analysis and reproducible research workflows.
Flexible Bayesian clustering framework with MCMC inference. Supports multiple nonparametric priors (DP, NGGP), distance-based models, and state-of-the-art samplers including Split-Merge algorithms. Built in C++ for efficient computations.
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