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

Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence

PM520 Advanced Statistical Computing

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

:notebook: 💪 An introductory workshop lecture on ensemble machine learning with pipelines using the sl3 R package

Intro to Statistical Data Analysis using R

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

Feng Li's Course Materials for Statistical Computing

Mathematical Foundations and Statistical Computing in R

Statistical Computing Concepts for Public Health Researchers

Aspects of numerical analysis in the field of data science (matrix inversion, splines, function optimization, bayesian statistics, MCMC, etc)

This installs a ready to use Posit R Environment on AWS EKS - See Readme for details

UCLA Biostat M257 - 2024 Spring

A modular R framework for data analysis, with emphasis on data processing and reproducible workflows.

Practical implementation of selected algorithms, concepts and techniques from data science, data analysis, data characterization and data visualization topics.

Statistical Computing Lab with R

UCLA Biostat M257 - 2025 Spring

Data Science portfolio showcasing healthcare analytics, machine learning, database systems, and statistical visualization projects. Master's student at University of Arizona seeking growth opportunities in biological/healthcare data science.

Nonconvex Accelerated Gradient Method developed by me; paper published at Statistics and Computing -- "Accelerated gradient methods for sparse statistical learning with nonconvex penalties"

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.

End-to-end Python implementation of Ma et al.'s (2025) matrix-variate diffusion index models for macroeconomic forecasting. Features α-PCA factor extraction, supervised screening, and ILS estimation for high-dimensional forecasting with preserved structural information.

CUHK Course code: STAT 3011 | This course is designed to strengthen students' ability in statistical computing as well as in processing and analysing data. Students are required to participate in several term projects with emphasis on techniques of data management and analysis.